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降维和特征提取 -凯发k8网页登录

pca、因子分析、特征选择、特征提取等

特征转换方法可以通过将数据转换为新特征来减少数据的维度。当无法转换变量时（例如，当数据中存在分类变量时），最好使用特征选择方法。有关特别适用于最小二乘拟合的特征选择方法，请参阅。

实时编辑器任务

reduce dimensionality using principal component analysis (pca) in live editor

函数

univariate feature ranking for classification using chi-square tests
rank features for classification using minimum redundancy maximum relevance (mrmr) algorithm
feature selection using neighborhood component analysis for classification
univariate feature ranking for regression using f-tests
rank features for regression using minimum redundancy maximum relevance (mrmr) algorithm
feature selection using neighborhood component analysis for regression
fsulaplacianrank features for unsupervised learning using laplacian scores
partialdependencecompute partial dependence
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
estimates of predictor importance for classification tree
predictorimportanceestimates of predictor importance for classification ensemble of decision trees
estimates of predictor importance for regression tree
estimates of predictor importance for regression ensemble
rank importance of predictors using relieff or rrelieff algorithm
sequential feature selection using custom criterion
perform stepwise regression
create generalized linear regression model by stepwise regression
ricafeature extraction by using reconstruction ica
sparsefiltfeature extraction by using sparse filtering
transform predictors into extracted features
t-distributed stochastic neighbor embedding
bartlett’s test
canonical correlation
原始数据的主成分分析
principal component analysis on covariance matrix
residuals from principal component analysis
probabilistic principal component analysis
factor analysis
rotate factor loadings
nonnegative matrix factorization
classical multidimensional scaling
mahalanobis distance to reference samples
nonclassical multidimensional scaling
成对观测值之间的两两距离
format distance matrix
procrustes analysis

对象

feature selection for classification using neighborhood component analysis (nca)
feature selection for regression using neighborhood component analysis (nca)
reconstructionicafeature extraction by reconstruction ica
feature extraction by sparse filtering

主题

特征选择

  • introduction to feature selection
    learn about feature selection algorithms and explore the functions available for 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) is a non-parametric method for selecting features with the goal of maximizing prediction accuracy of regression and classification algorithms.


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

  • select split-predictors for random forests using interaction test algorithm.

特征提取


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

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

  • this example shows how to use rica to disentangle mixed audio signals.

t-sne 多维可视化


  • 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.

  • this example shows how t-sne creates a useful low-dimensional embedding of high-dimensional data.

  • this example shows the effects of various tsne settings.

  • output function description and example for t-sne.

pca 和典型相关


  • 主成分分析通过用一组新变量替换几个相关变量来降低数据的维数，这些新变量是原始变量的线性组合。

  • perform a weighted principal components analysis and interpret the results.

因子分析


  • 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.

  • use factor analysis to investigate whether companies within the same sector experience similar week-to-week changes in stock prices.

  • this example shows how to perform factor analysis using statistics and machine learning toolbox™.

非负矩阵分解


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

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

多维尺度分析


  • 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.

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

  • this example shows how to perform classical multidimensional scaling using the cmdscale function in statistics and machine learning toolbox™.

  • this example shows how to visualize dissimilarity data using nonclassical forms of multidimensional scaling (mds).

  • perform nonclassical multidimensional scaling using mdscale.

普氏分析


  • use procrustes analysis to compare two handwritten numerals.
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