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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.

함수

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
fsrmrmrRank features for regression using minimum redundancy maximum relevance (MRMR) algorithm
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
mahal기준 표본까지의 마할라노비스 거리
mdscaleNonclassical multidimensional scaling
pdist관측값 쌍 간의 쌍별(Pairwise) 거리
squareformFormat distance matrix
procrustesProcrustes analysis

객체

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

특징 추출

t-SNE 다차원 시각화

  • 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.

PCA와 정준 상관

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

다차원 스케일링

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