Main Content

Esta página aún no se ha traducido para esta versión. Puede ver la versión más reciente de esta página en inglés.

Reducción de dimensionalidad y extracción de características

PCA, análisis de factores, selección de características, extracción de entidades y más

las técnicas reducen la dimensionalidad de los datos mediante la transformación de datos en nuevas características. las técnicas son preferibles cuando la transformación de variables no es posible, por ejemplo, cuando hay variables categóricas en los datos.Transformación de característicasSelección de características Para una técnica de selección de operaciones específicamente adecuada para el ajuste de mínimos cuadrados, véase .Regresión escalonada

Funciones

expandir todo

fscmrmrRank features for classification using minimum redundancy maximum relevance (MRMR) algorithm
fscncaFeature selection using neighborhood component analysis for classification
fsrncaFeature selection using neighborhood component analysis for regression
fsulaplacianRank features for unsupervised learning using Laplacian scores
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
oobPermutedPredictorImportancePredictor importance estimates by permutation of out-of-bag predictor observations for random forest of classification trees
oobPermutedPredictorImportancePredictor importance estimates by permutation of out-of-bag predictor observations for random forest of regression trees
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
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 componentes principales de datos sin procesar
pcacovPrincipal component analysis on covariance matrix
pcaresResiduals from principal component analysis
ppcaProbabilistic principal component analysis
factoranFactor analysis
rotatefactorsRotate factor loadings
nnmfNonnegative matrix factorization
cmdscaleClassical multidimensional scaling
mahalDistancia Mahalanobis
mdscaleNonclassical multidimensional scaling
pdistDistancia por pares entre pares de observaciones
squareformFormat distance matrix
procrustesProcrustes analysis

Objetos

expandir todo

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

Temas

Selección de características

Introduction to Feature Selection

Learn about feature selection algorithms and explore the functions available for feature selection.

Sequential Feature Selection

This topic introduces to sequential feature selection and provides an example that selects features sequentially using a custom criterion and the sequentialfs function.

Neighborhood Component Analysis (NCA) Feature Selection

Neighborhood component analysis (NCA) is a non-parametric method for selecting features with the goal of maximizing prediction accuracy of regression and classification algorithms.

Regularize Discriminant Analysis Classifier

Make a more robust and simpler model by removing predictors without compromising the predictive power of the model.

Select Predictors for Random Forests

Select split-predictors for random forests using interaction test algorithm.

Extracción de características

Feature Extraction

Feature extraction is a set of methods to extract high-level features from data.

Feature Extraction Workflow

This example shows a complete workflow for feature extraction from image data.

Extract Mixed Signals

This example shows how to use rica to disentangle mixed audio signals.

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 de componentes principales (PCA)

Análisis de componentes principales reduce la dimensionalidad de los datos reemplazando varias variables correlacionadas por un nuevo conjunto de variables que son combinaciones lineales de las variables originales.

Analyze Quality of Life in U.S. Cities Using PCA

Perform a weighted principal components analysis and interpret the results.

Análisis de factores

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.

Factorización de matriz no negativa

Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a dimension-reduction technique based on a low-rank approximation of the feature space.

Perform Nonnegative Matrix Factorization

Perform nonnegative matrix factorization using the multiplicative and alternating least-squares algorithms.

Escalado multidimensional

Multidimensional Scaling

Multidimensional scaling allows you to visualize how near points are to each other for many kinds of distance or dissimilarity metrics and can produce a representation of data in a small number of dimensions.

Classical Multidimensional Scaling

Use cmdscale to perform classical (metric) multidimensional scaling, also known as principal coordinates analysis.

Nonclassical and Nonmetric Multidimensional Scaling

Perform nonclassical multidimensional scaling using mdscale.

Análisis de Procrustes

Procrustes Analysis

Procrustes analysis minimizes the differences in location between compared landmark data using the best shape-preserving Euclidean transformations.

Compare Handwritten Shapes Using Procrustes Analysis

Use Procrustes analysis to compare two handwritten numerals.

Ejemplos destacados