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Reducción de la dimensionalidad y extracción de características

PCA, análisis factorial, selección de características, extracción de características y mucho más

Las técnicas de transformación de características reducen la dimensionalidad de los datos transformándolos en nuevas características. Las técnicas de selección de características son preferibles cuando no es posible la transformación de las variables, p. ej., cuando los datos contienen variables categóricas. Para ver una técnica de selección de características indicada específicamente para el ajuste de mínimos cuadrados, consulte Regresión escalonada.

Tareas de Live Editor

Reduce DimensionalityReduce dimensionality using Principal Component Analysis (PCA) in Live Editor (desde R2022b)


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fscchi2Univariate feature ranking for classification using chi-square tests (desde R2020a)
fscmrmrRank features for classification using minimum redundancy maximum relevance (MRMR) algorithm (desde R2019b)
fscncaFeature selection using neighborhood component analysis for classification
fsrftestUnivariate feature ranking for regression using F-tests (desde R2020a)
fsrmrmrRank features for regression using minimum redundancy maximum relevance (MRMR) algorithm (desde R2022a)
fsrncaFeature selection using neighborhood component analysis for regression
fsulaplacianRank features for unsupervised learning using Laplacian scores (desde R2019b)
partialDependenceCompute partial dependence (desde R2020b)
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
oobPermutedPredictorImportanceOut-of-bag predictor importance estimates for random forest of classification trees by permutation
oobPermutedPredictorImportanceOut-of-bag predictor importance estimates for random forest of regression trees by permutation
predictorImportanceEstimates of predictor importance for classification tree
predictorImportanceEstimates of predictor importance for classification ensemble of decision trees
predictorImportanceEstimates of predictor importance for regression tree
predictorImportanceEstimates of predictor importance for regression ensemble of decision trees
relieffRank importance of predictors using ReliefF or RReliefF algorithm
sequentialfsSequential feature selection using custom criterion
stepwiselmPerform stepwise regression
stepwiseglmCreate generalized linear regression model by stepwise regression
ricaFeature extraction by using reconstruction ICA
sparsefiltFeature extraction by using sparse filtering
transformTransform predictors into extracted features
tsnet-Distributed Stochastic Neighbor Embedding
barttestBartlett’s test
canoncorrCanonical correlation
pcaAnálisis de los componentes principales de datos sin procesar
pcacovAnálisis de componentes principales en una matriz de covarianzas
pcaresResiduals from principal component analysis
ppcaProbabilistic principal component analysis
incrementalPCAIncremental principal component analysis (desde R2024a)
fitFit principal component analysis model to streaming data (desde R2024a)
transformTransform data into principal component scores (desde R2024a)
resetReset incremental principal component analysis model (desde R2024a)
factoranFactor analysis
rotatefactorsRotate factor loadings
nnmfNonnegative matrix factorization
cmdscaleClassical multidimensional scaling
mahalDistancia de Mahalanobis respecto a muestras de referencia
mdscaleNonclassical multidimensional scaling
pdistDistancia por pares entre pares de observaciones
squareformDar formato a una matriz de distancia
procrustesProcrustes analysis


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FeatureSelectionNCAClassificationFeature selection for classification using neighborhood component analysis (NCA)
FeatureSelectionNCARegressionFeature selection for regression using neighborhood component analysis (NCA)
ReconstructionICAFeature extraction by reconstruction ICA
SparseFilteringFeature extraction by sparse filtering


Selección de características

Extracción de características

Visualización multidimensional t-SNE

  • t-SNE
    t-SNE is a method for visualizing high-dimensional data by nonlinear reduction to two or three dimensions, while preserving some features of the original data.
  • Visualize High-Dimensional Data Using t-SNE
    This example shows how t-SNE creates a useful low-dimensional embedding of high-dimensional data.
  • tsne Settings
    This example shows the effects of various tsne settings.
  • t-SNE Output Function
    Output function description and example for t-SNE.

PCA y correlación canónica

Análisis factorial

  • Factor Analysis
    Factor analysis is a way to fit a model to multivariate data to estimate interdependence of measured variables on a smaller number of unobserved (latent) factors.
  • Analyze Stock Prices Using Factor Analysis
    Use factor analysis to investigate whether companies within the same sector experience similar week-to-week changes in stock prices.
  • Perform Factor Analysis on Exam Grades
    This example shows how to perform factor analysis using Statistics and Machine Learning Toolbox™.

Factorización de matrices no negativas

Escalas multidimensionales

Análisis de Procrustes