基于MATLAB的t-SNE算法多合成数据集降维可视化实现

1. 合成数据集生成与预处理

% 生成5类合成数据集
n_samples = 1000;
rng(42); % 固定随机种子

% 创建数据集
datasets = struct();
datasets.blobs = [make_blobs(n_samples,5,1.0,1,0)];
datasets.moons = [make_moons(n_samples,0.1,1)];
datasets.circles = [make_circles(n_samples,0.05,0.5,1)];
datasets.spiral = spiral_data(n_samples); % 自定义螺旋生成函数
datasets.random = rand(n_samples,2);

% 合并数据与标签
X_list = cell(1,5);
y_list = cell(1,5);
for i = 1:5
    X_list{
   
   i} = datasets.(['blobs','moons','circles','spiral','random]){
   
   i};
    y_list{
   
   i} = datasets.(['blobs','moons','circles','spiral','random]){
   
   i}.Y;
end

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2. t-SNE降维核心实现

% 统一参数设置
tsne_opts = struct(...
    'NumDimensions',2,...
    'Perplexity',30,...
    'LearnRate',200,...
    'NumIterations',1000,...
    'Algorithm','barnes-hut'); % 更高效近似算法

% 执行降维
results = cell