Algorithms that describe high-dimensional data with a few well-chosen quantities. Principal component analysis finds the directions along which the data vary most, linear discriminant analysis finds directions that best separate known classes, t-distributed stochastic neighbor embedding places similar items near each other on a two-dimensional map, and non-negative matrix factorization breaks a table of values into parts that can be added together.
Dimensionality Reduction and Matrix Factorization Algorithms
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t-Distributed Stochastic Neighbor Embedding (Wikipedia)
Has Member: t-Distributed Stochastic Neighbor Embedding, LeadQuote, Has Member: t-Distributed Stochastic Neighbor Embedding, Lead
t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.
scikit-learn: scikit-learn: Decomposing signals in components
scikit-learnHas Member: Principal Component Analysis Algorithm, Section 2.5.1 PCA
PCA is used to decompose a multivariate dataset in a set of successive orthogonal components that explain a maximum amount of the variance.
Has Member: Non-Negative Matrix Factorization Algorithm, Section 2.5.7 NMF
NMF can be plugged in instead of PCA or its variants, in the cases where the data matrix does not contain negative values.
View the Source scikit-learn: scikit-learn: Linear and Quadratic Discriminant Analysis
scikit-learnHas Member: Linear Discriminant Analysis Algorithm, Section 1.2.1Quote, Has Member: Linear Discriminant Analysis Algorithm, Section 1.2.1
LinearDiscriminantAnalysis can be used to perform supervised dimensionality reduction, by projecting the input data to a linear subspace consisting of the directions which maximize the separation between classes (in a precise sense discussed in the mathematics section below).
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