La traducción de esta página aún no se ha actualizado a la versión más reciente. Haga clic aquí para ver la última versión en inglés.

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 Dimensionality

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

Funciones

expandir todo

Selección de características

`fscchi2`	Univariate feature ranking for classification using chi-square tests
`fscmrmr`	Rank features for classification using minimum redundancy maximum relevance (MRMR) algorithm
`fscnca`	Feature selection using neighborhood component analysis for classification
`fsrftest`	Univariate feature ranking for regression using F-tests
`fsrmrmr`	Rank features for regression using minimum redundancy maximum relevance (MRMR) algorithm
`fsrnca`	Feature selection using neighborhood component analysis for regression
`fsulaplacian`	Rank features for unsupervised learning using Laplacian scores
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`oobPermutedPredictorImportance`	Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of classification trees
`oobPermutedPredictorImportance`	Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of regression trees
`predictorImportance`	Estimates of predictor importance for classification tree
`predictorImportance`	Estimates of predictor importance for classification ensemble of decision trees
`predictorImportance`	Estimates of predictor importance for regression tree
`predictorImportance`	Estimates of predictor importance for regression ensemble
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`sequentialfs`	Sequential feature selection using custom criterion
`stepwiselm`	Perform stepwise regression
`stepwiseglm`	Create generalized linear regression model by stepwise regression

Extracción de características

`rica`	Feature extraction by using reconstruction ICA
`sparsefilt`	Feature extraction by using sparse filtering
`transform`	Transform predictors into extracted features

Visualización multidimensional t-SNE

tsne t-Distributed Stochastic Neighbor Embedding

PCA y correlación canónica

`barttest`	Bartlett’s test
`canoncorr`	Canonical correlation
`pca`	Análisis de los componentes principales de datos sin procesar
`pcacov`	Principal component analysis on covariance matrix
`pcares`	Residuals from principal component analysis
`ppca`	Probabilistic principal component analysis

Análisis factorial

`factoran`	Factor analysis
`rotatefactors`	Rotate factor loadings

Factorización de matrices no negativas

nnmf Nonnegative matrix factorization

Escalas multidimensionales

`cmdscale`	Classical multidimensional scaling
`mahal`	Mahalanobis distance to reference samples
`mdscale`	Nonclassical multidimensional scaling
`pdist`	Distancia por pares entre pares de observaciones
`squareform`	Format distance matrix

Análisis de Procrustes

procrustes Procrustes analysis

Objetos

expandir todo

Selección de características

`FeatureSelectionNCAClassification`	Feature selection for classification using neighborhood component analysis (NCA)
`FeatureSelectionNCARegression`	Feature selection for regression using neighborhood component analysis (NCA)

Extracción de características

`ReconstructionICA`	Feature extraction by reconstruction ICA
`SparseFiltering`	Feature 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 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.
- Robust Feature Selection Using NCA for Regression
- Tune Regularization Parameter to Detect Features Using NCA for Classification
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)
El análisis de componentes principales reduce la dimensionalidad de los datos sustituyendo 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 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

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.

Escalas multidimensionales

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.
Classical Multidimensional Scaling Applied to Nonspatial Distances
This example shows how to perform classical multidimensional scaling using the cmdscale function in Statistics and Machine Learning Toolbox™.
Nonclassical Multidimensional Scaling
This example shows how to visualize dissimilarity data using nonclassical forms of multidimensional scaling (MDS).
Nonclassical and Nonmetric Multidimensional Scaling
Perform nonclassical multidimensional scaling using mdscale.

Análisis de Procrustes

Compare Handwritten Shapes Using Procrustes Analysis
Use Procrustes analysis to compare two handwritten numerals.

Ejemplos destacados

Select Features for Classifying High-Dimensional Data

Select features for classifying high-dimensional data. More specifically, it shows how to perform sequential feature selection, which is one of the most popular feature selection algorithms. It also shows how to use holdout and cross-validation to evaluate the performance of the selected features.

Abrir script en vivo

Partial Least Squares Regression and Principal Components Regression

Apply partial least squares regression (PLSR) and principal components regression (PCR), and explore the effectiveness of the two methods.

Abrir script en vivo

Fitting an Orthogonal Regression Using Principal Components Analysis

Use Principal Components Analysis (PCA) to fit a linear regression. PCA minimizes the perpendicular distances from the data to the fitted model. This is the linear case of what is known as Orthogonal Regression or Total Least Squares, and is appropriate when there is no natural distinction between predictor and response variables, or when all variables are measured with error. This is in contrast to the usual regression assumption that predictor variables are measured exactly, and only the response variable has an error component.

Abrir script en vivo