sklearn.decomposition

Matrix decomposition algorithms.

These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be regarded as dimensionality reduction techniques.

User guide. See the Decomposing signals in components (matrix factorization problems) section for further details.

DictionaryLearning	Dictionary learning.
FactorAnalysis	Factor Analysis (FA).
FastICA	FastICA: a fast algorithm for Independent Component Analysis.
IncrementalPCA	Incremental principal components analysis (IPCA).
KernelPCA	Kernel Principal component analysis (KPCA).
LatentDirichletAllocation	Latent Dirichlet Allocation with online variational Bayes algorithm.
MiniBatchDictionaryLearning	Mini-batch dictionary learning.
MiniBatchNMF	Mini-Batch Non-Negative Matrix Factorization (NMF).
MiniBatchSparsePCA	Mini-batch Sparse Principal Components Analysis.
NMF	Non-Negative Matrix Factorization (NMF).
PCA	Principal component analysis (PCA).
SparseCoder	Sparse coding.
SparsePCA	Sparse Principal Components Analysis (SparsePCA).
TruncatedSVD	Dimensionality reduction using truncated SVD (aka LSA).
dict_learning	Solve a dictionary learning matrix factorization problem.
dict_learning_online	Solve a dictionary learning matrix factorization problem online.
fastica	Perform Fast Independent Component Analysis.
non_negative_factorization	Compute Non-negative Matrix Factorization (NMF).
sparse_encode	Sparse coding.