基于 TGLVM 算法的迁移学习分类系统

bubiyoushang8882026-05-14 9:30

一、TGLVM 算法

TGLVM (Transfer Learning with Gaussian Latent Variable Models) 的核心思想是通过高斯潜变量模型建立源域和目标域之间的概率映射关系，实现知识的有效迁移。

二、MATLAB 实现

2.1 主程序 (tglvm_transfer_learning.m)

matlab 复制代码 
%% 基于TGLVM算法的迁移学习分类系统
% 功能：使用TGLVM算法对两类数据实施迁移学习分类

clear all; close all; clc;

fprintf('=== 基于TGLVM算法的迁移学习分类系统 ===\n\n');

%% 1. 参数配置
config = struct();
config.latent_dim = 10;          % 潜变量维度
config.source_samples = 200;     % 源域样本数
config.target_samples = 150;     % 目标域样本数
config.num_classes = 2;         % 类别数
config.max_iterations = 200;    % 最大迭代次数
config.learning_rate = 0.01;    % 学习率
config.reg_param = 0.001;       % 正则化参数
config.domain_weight = 0.5;     % 域适应权重

fprintf('系统配置：\n');
fprintf('  潜变量维度: %d\n', config.latent_dim);
fprintf('  源域样本数: %d\n', config.source_samples);
fprintf('  目标域样本数: %d\n', config.target_samples);
fprintf('  类别数: %d\n', config.num_classes);
fprintf('  最大迭代次数: %d\n\n', config.max_iterations);

%% 2. 生成模拟数据
fprintf('生成模拟数据...\n');
[data] = generate_simulation_data(config);

fprintf('数据信息：\n');
fprintf('  源域特征维度: %d\n', size(data.source_features, 2));
fprintf('  目标域特征维度: %d\n', size(data.target_features, 2));
fprintf('  源域标签分布: [类0: %d, 类1: %d]\n', ...
        sum(data.source_labels == 0), sum(data.source_labels == 1));
fprintf('  目标域标签分布: [类0: %d, 类1: %d]\n\n', ...
        sum(data.target_labels == 0), sum(data.target_labels == 1));

%% 3. 初始化TGLVM模型
fprintf('初始化TGLVM模型...\n');
[tglvm_model] = initialize_tglvm_model(data, config);

fprintf('TGLVM模型结构：\n');
fprintf('  源域潜变量维度: %d\n', config.latent_dim);
fprintf('  目标域潜变量维度: %d\n', config.latent_dim);
fprintf('  共享潜变量维度: %d\n\n', config.latent_dim);

%% 4. 训练TGLVM模型
fprintf('开始训练TGLVM模型...\n');
tic;
[trained_model, training_history] = train_tglvm_model(tglvm_model, data, config);
training_time = toc;

fprintf('训练完成！\n');
fprintf('  训练时间: %.2f 秒\n', training_time);
fprintf('  最终训练损失: %.6f\n', training_history.final_loss);
fprintf('  最终域适应损失: %.6f\n\n', training_history.final_domain_loss);

%% 5. 迁移学习分类
fprintf('执行迁移学习分类...\n');
[classification_results] = perform_transfer_classification(trained_model, data, config);

fprintf('分类结果：\n');
fprintf('  源域分类准确率: %.2f%%\n', classification_results.source_accuracy * 100);
fprintf('  目标域分类准确率: %.2f%%\n', classification_results.target_accuracy * 100);
fprintf('  迁移提升: %.2f%%\n\n', classification_results.transfer_improvement * 100);

%% 6. 结果可视化
fprintf('生成可视化结果...\n');
visualize_tglvm_results(trained_model, data, classification_results, training_history, config);

%% 7. 保存模型和结果
save('tglvm_transfer_model.mat', 'trained_model', 'config', 'classification_results');
fprintf('模型已保存到: tglvm_transfer_model.mat\n');

fprintf('\n=== TGLVM迁移学习分类系统运行完成 ===\n');

2.2 数据生成模块 (generate_simulation_data.m)

matlab 复制代码 
function [data] = generate_simulation_data(config)
    % 生成模拟的迁移学习数据
    
    rng(42); % 固定随机种子
    
    % 生成源域数据（有标签）
    source_features = zeros(config.source_samples, 50);
    source_labels = zeros(config.source_samples, 1);
    
    % 类别0的特征分布
    class0_samples = config.source_samples / 2;
    source_features(1:class0_samples, :) = mvnrnd(zeros(1, 50), eye(50), class0_samples);
    source_labels(1:class0_samples) = 0;
    
    % 类别1的特征分布
    source_features(class0_samples+1:end, :) = mvnrnd(ones(1, 50) * 2, eye(50), class0_samples);
    source_labels(class0_samples+1:end) = 1;
    
    % 添加噪声
    source_features = source_features + 0.1 * randn(size(source_features));
    
    % 生成目标域数据（无标签，但有域偏移）
    target_features = zeros(config.target_samples, 50);
    target_labels = zeros(config.target_samples, 1);
    
    % 目标域的域偏移（模拟不同的数据分布）
    offset = 1.5;
    
    % 类别0在目标域的分布
    target_features(1:class0_samples, :) = mvnrnd(ones(1, 50) * offset, eye(50) * 1.2, class0_samples);
    target_labels(1:class0_samples) = 0;
    
    % 类别1在目标域的分布
    target_features(class0_samples+1:end, :) = mvnrnd(ones(1, 50) * (offset + 2), eye(50) * 1.2, ...
                                                      config.target_samples - class0_samples);
    target_labels(class0_samples+1:end) = 1;
    
    % 添加噪声
    target_features = target_features + 0.15 * randn(size(target_features));
    
    % 数据归一化
    [source_features, source_ps] = mapminmax(source_features', 0, 1);
    source_features = source_features';
    
    [target_features, target_ps] = mapminmax('apply', target_features', source_ps);
    target_features = target_features';
    
    % 组织数据
    data.source_features = source_features;
    data.source_labels = source_labels;
    data.target_features = target_features;
    data.target_labels = target_labels;
    data.source_ps = source_ps;
    data.target_ps = target_ps;
    
    fprintf('  模拟数据生成完成\n');
end

2.3 TGLVM 模型初始化 (initialize_tglvm_model.m)

matlab 复制代码 
function [tglvm_model] = initialize_tglvm_model(data, config)
    % 初始化TGLVM模型参数
    
    feature_dim = size(data.source_features, 2);
    
    % 源域模型参数
    tglvm_model.source.W = randn(feature_dim, config.latent_dim) * 0.1; % 映射矩阵
    tglvm_model.source.mu = zeros(config.latent_dim, 1); % 潜变量均值
    tglvm_model.source.Sigma = eye(config.latent_dim); % 潜变量协方差
    tglvm_model.source.sigma2 = 1.0; % 观测噪声方差
    
    % 目标域模型参数
    tglvm_model.target.W = randn(feature_dim, config.latent_dim) * 0.1; % 映射矩阵
    tglvm_model.target.mu = zeros(config.latent_dim, 1); % 潜变量均值
    tglvm_model.target.Sigma = eye(config.latent_dim); % 潜变量协方差
    tglvm_model.target.sigma2 = 1.0; % 观测噪声方差
    
    % 共享潜变量模型参数
    tglvm_model.shared.mu = zeros(config.latent_dim, 1); % 共享潜变量均值
    tglvm_model.shared.Sigma = eye(config.latent_dim); % 共享潜变量协方差
    
    % 分类器参数
    tglvm_model.classifier.W = randn(config.latent_dim, config.num_classes) * 0.1; % 分类权重
    tglvm_model.classifier.b = zeros(config.num_classes, 1); % 分类偏置
    
    % 域适应参数
    tglvm_model.domain_adaptation.source_to_shared = eye(config.latent_dim); % 源域到共享的变换
    tglvm_model.domain_adaptation.target_to_shared = eye(config.latent_dim); % 目标域到共享的变换
    
    fprintf('  TGLVM模型初始化完成\n');
end

2.4 TGLVM 模型训练 (train_tglvm_model.m)

matlab 复制代码 
function [trained_model, training_history] = train_tglvm_model(tglvm_model, data, config)
    % 训练TGLVM模型
    
    fprintf('  开始TGLVM模型训练...\n');
    
    % 初始化训练历史
    training_history.loss = zeros(config.max_iterations, 1);
    training_history.domain_loss = zeros(config.max_iterations, 1);
    training_history.classification_loss = zeros(config.max_iterations, 1);
    
    % 训练循环
    for iter = 1:config.max_iterations
        % 1. E步：推断潜变量
        [source_latent, target_latent, shared_latent] = e_step(tglvm_model, data, config);
        
        % 2. M步：更新模型参数
        tglvm_model = m_step(tglvm_model, data, source_latent, target_latent, shared_latent, config);
        
        % 3. 计算损失
        loss = compute_total_loss(tglvm_model, data, source_latent, target_latent, shared_latent, config);
        domain_loss = compute_domain_loss(tglvm_model, source_latent, target_latent, shared_latent, config);
        classification_loss = compute_classification_loss(tglvm_model, data, source_latent, config);
        
        training_history.loss(iter) = loss;
        training_history.domain_loss(iter) = domain_loss;
        training_history.classification_loss(iter) = classification_loss;
        
        % 显示进度
        if mod(iter, 20) == 0
            fprintf('    迭代 %d/%d: 总损失=%.6f, 域损失=%.6f, 分类损失=%.6f\n', ...
                    iter, config.max_iterations, loss, domain_loss, classification_loss);
        end
    end
    
    trained_model = tglvm_model;
    fprintf('  TGLVM模型训练完成\n');
end

function [source_latent, target_latent, shared_latent] = e_step(model, data, config)
    % E步：推断潜变量
    
    % 源域潜变量推断
    source_latent = zeros(size(data.source_features, 1), config.latent_dim);
    for i = 1:size(data.source_features, 1)
        x = data.source_features(i, :)';
        W = model.source.W;
        Sigma = model.source.Sigma;
        sigma2 = model.source.sigma2;
        
        % 计算后验分布
        inv_term = inv(W' * W / sigma2 + inv(Sigma));
        mu_post = inv_term * (W' * x / sigma2 + inv(Sigma) * model.source.mu);
        
        source_latent(i, :) = mvnrnd(mu_post', inv_term)';
    end
    
    % 目标域潜变量推断
    target_latent = zeros(size(data.target_features, 1), config.latent_dim);
    for i = 1:size(data.target_features, 1)
        x = data.target_features(i, :)';
        W = model.target.W;
        Sigma = model.target.Sigma;
        sigma2 = model.target.sigma2;
        
        % 计算后验分布
        inv_term = inv(W' * W / sigma2 + inv(Sigma));
        mu_post = inv_term * (W' * x / sigma2 + inv(Sigma) * model.target.mu);
        
        target_latent(i, :) = mvnrnd(mu_post', inv_term)';
    end
    
    % 共享潜变量推断
    shared_latent = zeros(size(source_latent, 1) + size(target_latent, 1), config.latent_dim);
    
    % 简单平均作为共享潜变量（实际应更复杂）
    all_latent = [source_latent; target_latent];
    shared_latent = all_latent;
end

function model = m_step(model, data, source_latent, target_latent, shared_latent, config)
    % M步：更新模型参数
    
    % 更新源域映射矩阵
    X_source = data.source_features;
    Z_source = source_latent;
    model.source.W = (X_source' * Z_source) / (Z_source' * Z_source + config.reg_param * eye(config.latent_dim));
    
    % 更新目标域映射矩阵
    X_target = data.target_features;
    Z_target = target_latent;
    model.target.W = (X_target' * Z_target) / (Z_target' * Z_target + config.reg_param * eye(config.latent_dim));
    
    % 更新共享潜变量参数
    model.shared.mu = mean(shared_latent, 1)';
    model.shared.Sigma = cov(shared_latent) + config.reg_param * eye(config.latent_dim);
    
    % 更新分类器参数
    labels_onehot = ind2vec(data.source_labels' + 1)';
    model.classifier.W = (Z_source' * labels_onehot) / (Z_source' * Z_source + config.reg_param * eye(config.latent_dim));
    model.classifier.b = mean(labels_onehot - Z_source * model.classifier.W, 1)';
    
    % 更新域适应变换
    model.domain_adaptation.source_to_shared = model.shared.Sigma * inv(model.source.Sigma);
    model.domain_adaptation.target_to_shared = model.shared.Sigma * inv(model.target.Sigma);
end

function loss = compute_total_loss(model, data, source_latent, target_latent, shared_latent, config)
    % 计算总损失
    
    % 重构损失
    source_recon = source_latent * model.source.W';
    target_recon = target_latent * model.target.W';
    
    source_recon_loss = mean(mean((data.source_features - source_recon).^2));
    target_recon_loss = mean(mean((data.target_features - target_recon).^2));
    
    % 潜变量分布损失
    source_latent_loss = mean(diag((source_latent - model.source.mu') * inv(model.source.Sigma) * (source_latent - model.source.mu')));
    target_latent_loss = mean(diag((target_latent - model.target.mu') * inv(model.target.Sigma) * (target_latent - model.target.mu')));
    
    % 共享潜变量损失
    shared_loss = mean(diag((shared_latent - model.shared.mu') * inv(model.shared.Sigma) * (shared_latent - model.shared.mu')));
    
    loss = source_recon_loss + target_recon_loss + source_latent_loss + target_latent_loss + shared_loss;
end

function domain_loss = compute_domain_loss(model, source_latent, target_latent, shared_latent, config)
    % 计算域适应损失
    
    % 计算源域和目标域潜变量分布之间的距离
    source_mean = mean(source_latent, 1);
    target_mean = mean(target_latent, 1);
    
    % 最大均值差异 (MMD)
    mmd = sum((source_mean - target_mean).^2);
    
    % 协方差差异
    source_cov = cov(source_latent);
    target_cov = cov(target_latent);
    cov_diff = sum(sum((source_cov - target_cov).^2));
    
    domain_loss = mmd + cov_diff;
end

function classification_loss = compute_classification_loss(model, data, source_latent, config)
    % 计算分类损失
    
    % 计算预测概率
    logits = source_latent * model.classifier.W + model.classifier.b';
    probs = exp(logits) ./ sum(exp(logits), 2);
    
    % 交叉熵损失
    labels_onehot = ind2vec(data.source_labels' + 1)';
    epsilon = 1e-10; % 防止log(0)
    classification_loss = -mean(mean(labels_onehot .* log(probs + epsilon)));
end

2.5 迁移学习分类 (perform_transfer_classification.m)

matlab 复制代码 
function [classification_results] = perform_transfer_classification(model, data, config)
    % 执行迁移学习分类
    
    fprintf('  执行迁移学习分类...\n');
    
    % 1. 源域分类
    source_latent = infer_latent_variables(model, data.source_features, 'source');
    source_predictions = classify_latent_variables(model, source_latent);
    source_accuracy = sum(source_predictions == data.source_labels) / length(data.source_labels);
    
    % 2. 目标域分类（无迁移）
    target_latent_no_transfer = infer_latent_variables(model, data.target_features, 'target');
    target_predictions_no_transfer = classify_latent_variables(model, target_latent_no_transfer);
    target_accuracy_no_transfer = sum(target_predictions_no_transfer == data.target_labels) / length(data.target_labels);
    
    % 3. 目标域分类（有迁移）
    % 应用域适应变换
    target_latent_transferred = apply_domain_adaptation(model, target_latent_no_transfer);
    target_predictions_transfer = classify_latent_variables(model, target_latent_transferred);
    target_accuracy_transfer = sum(target_predictions_transfer == data.target_labels) / length(data.target_labels);
    
    % 4. 计算迁移提升
    transfer_improvement = target_accuracy_transfer - target_accuracy_no_transfer;
    
    % 5. 基线分类器比较（不使用TGLVM）
    baseline_accuracy = compare_with_baseline(data);
    
    % 存储结果
    classification_results.source_accuracy = source_accuracy;
    classification_results.target_accuracy_no_transfer = target_accuracy_no_transfer;
    classification_results.target_accuracy_transfer = target_accuracy_transfer;
    classification_results.transfer_improvement = transfer_improvement;
    classification_results.baseline_accuracy = baseline_accuracy;
    
    fprintf('  迁移学习分类完成\n');
end

function latent_variables = infer_latent_variables(model, features, domain)
    % 推断潜变量
    
    latent_variables = zeros(size(features, 1), size(model.source.W, 2));
    
    if strcmp(domain, 'source')
        W = model.source.W;
        mu = model.source.mu;
        Sigma = model.source.Sigma;
    else
        W = model.target.W;
        mu = model.target.mu;
        Sigma = model.target.Sigma;
    end
    
    for i = 1:size(features, 1)
        x = features(i, :)';
        inv_term = inv(W' * W + inv(Sigma));
        mu_post = inv_term * (W' * x + inv(Sigma) * mu);
        latent_variables(i, :) = mu_post';
    end
end

function predictions = classify_latent_variables(model, latent_variables)
    % 分类潜变量
    
    logits = latent_variables * model.classifier.W + model.classifier.b';
    [~, predictions] = max(logits, [], 2);
    predictions = predictions - 1; % 转换为0,1标签
end

function transferred_latent = apply_domain_adaptation(model, target_latent)
    % 应用域适应变换
    
    % 简单线性变换
    transferred_latent = target_latent * model.domain_adaptation.target_to_shared';
end

function baseline_accuracy = compare_with_baseline(data)
    % 与基线分类器比较
    
    % 使用SVM作为基线
    svm_model = fitcsvm(data.source_features, data.source_labels);
    predictions = predict(svm_model, data.target_features);
    baseline_accuracy = sum(predictions == data.target_labels) / length(data.target_labels);
end

2.6 可视化模块 (visualize_tglvm_results.m)

matlab 复制代码 
function visualize_tglvm_results(model, data, classification_results, training_history, config)
    % 可视化TGLVM结果
    
    figure('Position', [100, 100, 1400, 900]);
    
    % 1. 训练损失曲线
    subplot(3, 4, 1);
    plot(1:config.max_iterations, training_history.loss, 'b-', 'LineWidth', 2);
    xlabel('迭代次数');
    ylabel('总损失');
    title('TGLVM训练损失曲线');
    grid on;
    
    % 2. 域适应损失曲线
    subplot(3, 4, 2);
    plot(1:config.max_iterations, training_history.domain_loss, 'r-', 'LineWidth', 2);
    xlabel('迭代次数');
    ylabel('域适应损失');
    title('域适应损失曲线');
    grid on;
    
    % 3. 分类损失曲线
    subplot(3, 4, 3);
    plot(1:config.max_iterations, training_history.classification_loss, 'g-', 'LineWidth', 2);
    xlabel('迭代次数');
    ylabel('分类损失');
    title('分类损失曲线');
    grid on;
    
    % 4. 分类准确率对比
    subplot(3, 4, 4);
    accuracies = [classification_results.source_accuracy, ...
                  classification_results.target_accuracy_no_transfer, ...
                  classification_results.target_accuracy_transfer, ...
                  classification_results.baseline_accuracy] * 100;
    
    bar(1:4, accuracies, 'FaceColor', [0.2, 0.6, 0.8; 0.8, 0.2, 0.6; 0.2, 0.8, 0.4; 0.6, 0.6, 0.2], 'EdgeColor', 'k');
    set(gca, 'XTickLabel', {'源域', '目标域(无迁移)', '目标域(有迁移)', '基线SVM'});
    ylabel('准确率 (%)');
    title('分类准确率对比');
    ylim([0, 100]);
    grid on;
    
    % 5. 潜变量分布可视化 (PCA)
    subplot(3, 4, 5);
    source_latent = infer_latent_variables(model, data.source_features, 'source');
    target_latent_no_transfer = infer_latent_variables(model, data.target_features, 'target');
    target_latent_transferred = apply_domain_adaptation(model, target_latent_no_transfer);
    
    % PCA降维到2D
    all_latent = [source_latent; target_latent_no_transfer; target_latent_transferred];
    [coeff, score] = pca(all_latent);
    
    % 绘制散点图
    scatter(score(1:size(source_latent, 1), 1), score(1:size(source_latent, 1), 2), 20, 'b', 'filled');
    hold on;
    scatter(score(size(source_latent, 1)+1:size(source_latent, 1)+size(target_latent_no_transfer, 1), 1), ...
            score(size(source_latent, 1)+1:size(source_latent, 1)+size(target_latent_no_transfer, 1), 2), 20, 'r', 'filled');
    scatter(score(size(source_latent, 1)+size(target_latent_no_transfer, 1)+1:end, 1), ...
            score(size(source_latent, 1)+size(target_latent_no_transfer, 1)+1:end, 2), 20, 'g', 'filled');
    
    xlabel('第一主成分');
    ylabel('第二主成分');
    title('潜变量分布对比');
    legend('源域', '目标域(无迁移)', '目标域(有迁移)');
    grid on;
    
    % 6. 迁移提升效果
    subplot(3, 4, 6);
    improvement = classification_results.transfer_improvement * 100;
    bar(1, improvement, 'FaceColor', improvement > 0 ? 'g' : 'r', 'EdgeColor', 'k');
    xlabel('迁移学习');
    ylabel('准确率提升 (%)');
    title('迁移学习提升效果');
    grid on;
    
    % 7. 特征空间对齐效果
    subplot(3, 4, 7);
    % 计算源域和目标域潜变量的分布差异
    source_mean = mean(source_latent, 1);
    target_mean_no_transfer = mean(target_latent_no_transfer, 1);
    target_mean_transferred = mean(target_latent_transferred, 1);
    
    differences = [norm(source_mean - target_mean_no_transfer), norm(source_mean - target_mean_transferred)];
    bar(1:2, differences, 'FaceColor', [0.8, 0.4, 0.2; 0.2, 0.8, 0.4], 'EdgeColor', 'k');
    set(gca, 'XTickLabel', {'无迁移', '有迁移'});
    ylabel('分布差异 (欧氏距离)');
    title('特征空间对齐效果');
    grid on;
    
    % 8. 混淆矩阵
    subplot(3, 4, 8);
    target_predictions = classify_latent_variables(model, target_latent_transferred);
    confusion_mat = confusionmat(data.target_labels, target_predictions);
    imagesc(confusion_mat);
    colorbar;
    xlabel('预测类别');
    ylabel('真实类别');
    title('目标域混淆矩阵');
    set(gca, 'XTick', 1:2, 'XTickLabel', {'类0', '类1'});
    set(gca, 'YTick', 1:2, 'YTickLabel', {'类0', '类1'});
    
    % 在格子中显示数字
    for i = 1:2
        for j = 1:2
            text(j, i, num2str(confusion_mat(i, j)), ...
                 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', ...
                 'Color', 'white', 'FontWeight', 'bold');
        end
    end
    
    % 9. 模型参数可视化
    subplot(3, 4, 9);
    % 可视化映射矩阵的奇异值
    source_singular_values = svd(model.source.W);
    target_singular_values = svd(model.target.W);
    
    plot(1:length(source_singular_values), source_singular_values, 'b-o', 'LineWidth', 2, 'MarkerSize', 8);
    hold on;
    plot(1:length(target_singular_values), target_singular_values, 'r-s', 'LineWidth', 2, 'MarkerSize', 8);
    xlabel('奇异值索引');
    ylabel('奇异值大小');
    title('映射矩阵奇异值对比');
    legend('源域', '目标域');
    grid on;
    
    % 10. 分类器权重可视化
    subplot(3, 4, 10);
    classifier_weights = model.classifier.W;
    bar(1:size(classifier_weights, 1), classifier_weights(:, 1), 'b', 'EdgeColor', 'k');
    hold on;
    bar(1:size(classifier_weights, 1), classifier_weights(:, 2), 'r', 'EdgeColor', 'k');
    xlabel('潜变量维度');
    ylabel('分类权重');
    title('分类器权重分布');
    legend('类0权重', '类1权重');
    grid on;
    
    % 11. 域适应变换矩阵
    subplot(3, 4, 11);
    transformation_matrix = model.domain_adaptation.target_to_shared;
    imagesc(transformation_matrix);
    colorbar;
    xlabel('目标域潜变量');
    ylabel('共享潜变量');
    title('域适应变换矩阵');
    
    % 12. 综合性能评估
    subplot(3, 4, 12);
    axis off;
    
    performance_text = sprintf(['TGLVM迁移学习性能评估报告\n\n', ...
                              '源域性能:\n', ...
                              '  分类准确率: %.2f%%\n\n', ...
                              '目标域性能:\n', ...
                              '  无迁移准确率: %.2f%%\n', ...
                              '  有迁移准确率: %.2f%%\n', ...
                              '  迁移提升: %.2f%%\n\n', ...
                              '基线对比:\n', ...
                              '  基线SVM准确率: %.2f%%\n', ...
                              '  TGLVM相对提升: %.2f%%\n\n', ...
                              '模型复杂度:\n', ...
                              '  潜变量维度: %d\n', ...
                              '  源域映射矩阵: %dx%d\n', ...
                              '  目标域映射矩阵: %dx%d'],
                              classification_results.source_accuracy * 100, ...
                              classification_results.target_accuracy_no_transfer * 100, ...
                              classification_results.target_accuracy_transfer * 100, ...
                              classification_results.transfer_improvement * 100, ...
                              classification_results.baseline_accuracy * 100, ...
                              (classification_results.target_accuracy_transfer - classification_results.baseline_accuracy) * 100, ...
                              config.latent_dim, ...
                              size(model.source.W, 1), size(model.source.W, 2), ...
                              size(model.target.W, 1), size(model.target.W, 2));
    
    text(0.1, 0.5, performance_text, 'FontSize', 10, 'FontWeight', 'bold');
    
    sgtitle('基于TGLVM算法的迁移学习分类结果分析');
end

2.7 测试脚本 (test_tglvm_transfer.m)

matlab 复制代码 
%% TGLVM迁移学习分类测试脚本
clear all; close all; clc;

fprintf('=== TGLVM迁移学习分类测试 ===\n\n');

%% 测试1: 不同潜变量维度的影响
fprintf('测试1: 不同潜变量维度的影响\n');

latent_dims = [5, 10, 20, 30];
dimension_results = zeros(length(latent_dims), 4);

for i = 1:length(latent_dims)
    fprintf('  测试潜变量维度 %d...\n', latent_dims(i));
    
    % 创建配置
    config = struct();
    config.latent_dim = latent_dims(i);
    config.source_samples = 150;
    config.target_samples = 100;
    config.num_classes = 2;
    config.max_iterations = 100;
    config.learning_rate = 0.01;
    config.reg_param = 0.001;
    config.domain_weight = 0.5;
    
    % 生成数据
    data = generate_simulation_data(config);
    
    % 初始化模型
    model = initialize_tglvm_model(data, config);
    
    % 训练模型
    [trained_model, ~] = train_tglvm_model(model, data, config);
    
    % 测试分类
    results = perform_transfer_classification(trained_model, data, config);
    
    dimension_results(i, :) = [latent_dims(i), ...
                              results.source_accuracy * 100, ...
                              results.target_accuracy_transfer * 100, ...
                              results.transfer_improvement * 100];
end

% 可视化结果
figure('Position', [100, 100, 1200, 400]);
subplot(1, 3, 1);
plot(dimension_results(:, 1), dimension_results(:, 2), 'b-o', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('潜变量维度');
ylabel('源域准确率 (%)');
title('潜变量维度对源域准确率的影响');
grid on;

subplot(1, 3, 2);
plot(dimension_results(:, 1), dimension_results(:, 3), 'r-s', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('潜变量维度');
ylabel('目标域准确率 (%)');
title('潜变量维度对目标域准确率的影响');
grid on;

subplot(1, 3, 3);
plot(dimension_results(:, 1), dimension_results(:, 4), 'g-d', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('潜变量维度');
ylabel('迁移提升 (%)');
title('潜变量维度对迁移提升的影响');
grid on;

%% 测试2: 不同域适应权重的影响
fprintf('\n测试2: 不同域适应权重的影响\n');

domain_weights = [0.1, 0.3, 0.5, 0.7, 0.9];
weight_results = zeros(length(domain_weights), 3);

for i = 1:length(domain_weights)
    fprintf('  测试域适应权重 %.1f...\n', domain_weights(i));
    
    % 创建配置
    config = struct();
    config.latent_dim = 10;
    config.source_samples = 150;
    config.target_samples = 100;
    config.num_classes = 2;
    config.max_iterations = 100;
    config.learning_rate = 0.01;
    config.reg_param = 0.001;
    config.domain_weight = domain_weights(i);
    
    % 生成数据
    data = generate_simulation_data(config);
    
    % 初始化模型
    model = initialize_tglvm_model(data, config);
    
    % 训练模型
    [trained_model, ~] = train_tglvm_model(model, data, config);
    
    % 测试分类
    results = perform_transfer_classification(trained_model, data, config);
    
    weight_results(i, :) = [domain_weights(i), ...
                           results.target_accuracy_transfer * 100, ...
                           results.transfer_improvement * 100];
end

% 可视化结果
figure('Position', [100, 100, 800, 400]);
subplot(1, 2, 1);
plot(weight_results(:, 1), weight_results(:, 2), 'ro-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('域适应权重');
ylabel('目标域准确率 (%)');
title('域适应权重对目标域准确率的影响');
grid on;

subplot(1, 2, 2);
plot(weight_results(:, 1), weight_results(:, 3), 'gd-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('域适应权重');
ylabel('迁移提升 (%)');
title('域适应权重对迁移提升的影响');
grid on;

%% 测试3: 不同域偏移程度的鲁棒性测试
fprintf('\n测试3: 不同域偏移程度的鲁棒性测试\n');

domain_offsets = [0.5, 1.0, 1.5, 2.0, 3.0];
robustness_results = zeros(length(domain_offsets), 3);

for i = 1:length(domain_offsets)
    fprintf('  测试域偏移程度 %.1f...\n', domain_offsets(i));
    
    % 创建配置
    config = struct();
    config.latent_dim = 10;
    config.source_samples = 150;
    config.target_samples = 100;
    config.num_classes = 2;
    config.max_iterations = 100;
    config.learning_rate = 0.01;
    config.reg_param = 0.001;
    config.domain_weight = 0.5;
    
    % 生成数据（带不同偏移）
    data = generate_simulation_data_with_offset(config, domain_offsets(i));
    
    % 初始化模型
    model = initialize_tglvm_model(data, config);
    
    % 训练模型
    [trained_model, ~] = train_tglvm_model(model, data, config);
    
    % 测试分类
    results = perform_transfer_classification(trained_model, data, config);
    
    robustness_results(i, :) = [domain_offsets(i), ...
                              results.target_accuracy_transfer * 100, ...
                              results.transfer_improvement * 100];
end

% 可视化结果
figure('Position', [100, 100, 800, 400]);
subplot(1, 2, 1);
plot(robustness_results(:, 1), robustness_results(:, 2), 'mo-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('域偏移程度');
ylabel('目标域准确率 (%)');
title('不同域偏移程度下的目标域准确率');
grid on;

subplot(1, 2, 2);
plot(robustness_results(:, 1), robustness_results(:, 3), 'cd-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('域偏移程度');
ylabel('迁移提升 (%)');
title('不同域偏移程度下的迁移提升效果');
grid on;

fprintf('\n所有测试完成！\n');

function data = generate_simulation_data_with_offset(config, offset)
    % 生成带指定偏移的模拟数据
    rng(42);
    
    % 生成源域数据
    source_features = zeros(config.source_samples, 50);
    source_labels = zeros(config.source_samples, 1);
    
    class0_samples = config.source_samples / 2;
    source_features(1:class0_samples, :) = mvnrnd(zeros(1, 50), eye(50), class0_samples);
    source_features(class0_samples+1:end, :) = mvnrnd(ones(1, 50) * 2, eye(50), class0_samples);
    source_features = source_features + 0.1 * randn(size(source_features));
    
    % 生成目标域数据（带偏移）
    target_features = zeros(config.target_samples, 50);
    target_labels = zeros(config.target_samples, 1);
    
    target_features(1:class0_samples, :) = mvnrnd(ones(1, 50) * offset, eye(50) * 1.2, class0_samples);
    target_features(class0_samples+1:end, :) = mvnrnd(ones(1, 50) * (offset + 2), eye(50) * 1.2, ...
                                                      config.target_samples - class0_samples);
    target_features = target_features + 0.15 * randn(size(target_features));
    
    % 数据归一化
    [source_features, source_ps] = mapminmax(source_features', 0, 1);
    source_features = source_features';
    
    [target_features, ~] = mapminmax('apply', target_features', source_ps);
    target_features = target_features';
    
    % 组织数据
    data.source_features = source_features;
    data.source_labels = source_labels;
    data.target_features = target_features;
    data.target_labels = target_labels;
    data.source_ps = source_ps;
end

参考代码 利用tglvm算法对两类结果实施迁移学习分类 www.youwenfan.com/contentcsu/63372.html

三、应用

3.1 参数调优指南

参数 建议值 调整原则
潜变量维度 10-50 根据特征维度调整，通常为特征维度的1/5到1/2
域适应权重 0.3-0.7 域偏移大时增大权重，域偏移小时减小权重
学习率 0.001-0.1 根据收敛情况调整，过大导致震荡，过小收敛慢
正则化参数 0.001-0.01 防止过拟合，数据少时增大

3.2 工程实现要点

  1. 数据预处理：确保源域和目标域特征分布相似，必要时进行特征标准化
  2. 模型初始化：使用合理的初始化策略，避免局部最优
  3. 早停机制：监控验证集性能，防止过拟合
  4. 多尺度验证：在不同域偏移程度下验证模型鲁棒性

3.3 扩展功能建议

  1. 多源域迁移：扩展到多个源域的情况
  2. 深度TGLVM：结合深度神经网络增强特征表达能力
  3. 在线迁移：实现在线更新和适应
  4. 不确定性量化：提供分类结果的不确定性估计
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