Dimension Reduction

PCA

Matrix Decomposition method
project feature to max. variance

SVD

A better way to perform PCA that aovid huge computation of \(A^TA\)
sklearn is using SVD algo for PCA function

eigenvector

Manifold method

t-SNE

t-distributed stochastic neighbor embedding
Manifold method

class sklearn.manifold.TSNE(n_components=2, perplexity=30.0, early_exaggeration=12.0, learning_rate=200.0, n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07, metric='euclidean', init='random', verbose=0, random_state=None, method='barnes_hut', angle=0.5, n_jobs=None)

Barnes-Hut t-SNE

faster, limited to 2~3 dimension (but enough for visualization)