차원 축소 및 특징 추출

PCA, 인자 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.

라이브 편집기 작업

차원 축소

Reduce dimensionality using Principal Component Analysis (PCA) in Live Editor (R2022b 이후)

함수

모두 확장

특징 선택

`fscchi2`	Univariate feature ranking for classification using chi-square tests (R2020a 이후)
`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 (R2020a 이후)
`fsrmrmr`	Rank features for regression using minimum redundancy maximum relevance (MRMR) algorithm (R2022a 이후)
`fsrnca`	Feature selection using neighborhood component analysis for regression
`fsulaplacian`	Rank features for unsupervised learning using Laplacian scores
`partialDependence`	Compute partial dependence (R2020b 이후)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`oobPermutedPredictorImportance`	Out-of-bag predictor importance estimates for random forest of classification trees by permutation
`oobPermutedPredictorImportance`	Out-of-bag predictor importance estimates for random forest of regression trees by permutation
`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 of decision trees
`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

특징 추출

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

t-SNE 다차원 시각화

tsne t-분포 확률적 이웃 임베딩

PCA와 정준 상관

`barttest`	Bartlett’s test
`canoncorr`	Canonical correlation
`pca`	원시 데이터에 대한 주성분 분석
`pcacov`	공분산 행렬에 대한 주성분 분석
`pcares`	주성분 분석의 잔차
`ppca`	Probabilistic principal component analysis

점진적 PCA

`incrementalPCA`	Incremental principal component analysis (R2024a 이후)
`fit`	Fit principal component analysis model to streaming data (R2024a 이후)
`transform`	Transform data into principal component scores (R2024a 이후)
`reset`	Reset incremental principal component analysis model (R2024a 이후)

인자 분석

`factoran`	Factor analysis
`rotatefactors`	Rotate factor loadings

음이 아닌 행렬 분해

nnmf Nonnegative matrix factorization

다차원 스케일링

`cmdscale`	Classical multidimensional scaling
`mahal`	기준 표본까지의 마할라노비스 거리
`mdscale`	Nonclassical multidimensional scaling
`pdist`	관측값 쌍 간의 쌍별(Pairwise) 거리
`squareform`	거리 행렬 형식 지정

프로크루스테스 분석

procrustes Procrustes analysis

객체

모두 확장

특징 선택

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

특징 추출

`ReconstructionICA`	Feature extraction by reconstruction ICA
`SparseFiltering`	Feature 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 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.

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
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 선형 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.

인자 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.