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차원 축소 및 특징 추출

PCA, 요인 분석, 특징 선택, 특징 추출 등

특징 변환 기법은 데이터를 새 특징으로 변환하여 데이터의 차원 수를 줄입니다. 데이터에 categorical형 변수가 있는 경우와 같이 변수 변환이 가능하지 않은 경우 특징 선택 기법이 더 적합합니다. 특정적으로 최소제곱 피팅에 적합한 특징 선택 기법에 대한 자세한 내용은 단계적 회귀 항목을 참조하십시오.


모두 확장

fscchi2Univariate feature ranking for classification using chi-square tests
fscmrmrRank features for classification using minimum redundancy maximum relevance (MRMR) algorithm
fscncaFeature selection using neighborhood component analysis for classification
fsrftestUnivariate feature ranking for regression using F-tests
fsrncaFeature 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
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
pca원시 데이터에 대한 주성분 분석
pcacovPrincipal component analysis on covariance matrix
pcares주성분 분석의 잔차
ppcaProbabilistic principal component analysis
factoranFactor analysis
rotatefactorsRotate factor loadings
nnmfNonnegative matrix factorization
cmdscaleClassical multidimensional scaling
mahal마할라노비스 거리
mdscaleNonclassical multidimensional scaling
pdist관측값 쌍 간의 쌍별(Pairwise) 거리
squareformFormat distance matrix
procrustesProcrustes analysis


모두 확장

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

도움말 항목

특징 선택

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.

특징 추출

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.

t-SNE 다차원 시각화


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.

PCA와 정준 상관

주성분 분석(PCA)

주성분 분석은 상관관계가 있는 여러 변수를 원래 변수의 일차 결합인 새로운 변수의 집합으로 교체하여 데이터의 차원 수를 줄입니다.

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

Perform a weighted principal components analysis and interpret the results.

요인 분석

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™.

음이 아닌 행렬 분해

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.

다차원 스케일링

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.

프로크루스테스 분석

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.

추천 예제