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Dimensionality Reduction and Feature Extraction

PCA, factor analysis, feature selection, feature extraction, and more

功能转换techniques reduce the dimensionality in the data by transforming data into new features.Feature selection当不可能进行变量的转换时，例如，当数据中存在分类变量时，技术是可取的。有关专门适用于最小二乘配件的功能选择技术，请参见逐步回归。

功能

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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 使用单变量功能排名用于回归F-tests
fsrnca 使用邻里组件分析进行回归的特征选择
fsulaplacian 使用拉普拉斯分数的无监督学习的等级功能
partialDependence 计算部分依赖性
plotPartialDependence 创建部分依赖图（PDP）和个人条件期望（ICE）图
OOBPERMUTED PREDICTORIMPORTANE Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of classification trees
OOBPERMUTED PREDICTORIMPORTANE Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of regression trees
预测象征 Estimates of predictor importance for classification tree
预测象征 Estimates of predictor importance for classification ensemble of decision trees
预测象征 Estimates of predictor importance for regression tree
预测象征 预测因子对回归集合的重要性的估计值
relieff Rank importance of predictors using ReliefF or RReliefF algorithm
sequentialfs Sequential feature selection using custom criterion
stepwiselm 执行逐步回归
逐步Glm Create generalized linear regression model by stepwise regression
里卡 Feature extraction by using reconstruction ICA
稀疏 Feature extraction by using sparse filtering
transform 将预测因子转换为提取的特征
tsne t-Distributed Stochastic Neighbor Embedding
巴特斯特 Bartlett’s test
佳能 规范相关性
PCA 原始数据的主成分分析
PCAcov 协方差矩阵的主成分分析
PCAres Residuals from principal component analysis
ppca 概率主成分分析
factoran Factor analysis
旋转因素 旋转因子负载
nnmf 非负矩阵分解
cmdscale Classical multidimensional scaling
玛哈尔 Mahalanobis距离
mdscale Nonclassical multidimensional scaling
pdist Pairwise distance between pairs of observations
squareform 格式距离矩阵
procrustes procrustes分析

对象

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功能选择性分类 使用邻里组件分析（NCA）进行分类的特征选择
FeatureSelectionNCARegression 使用邻里组件分析（NCA）进行回归的特征选择
ReconstructionICA 重建ICA提取功能
SparseFiltering 通过稀疏特征提取过滤

话题

Feature Selection

功能选择简介

了解功能选择算法并探索可用于特征选择的功能。

Sequential Feature Selection

此主题介绍了顺序的功能选择，并提供了一个示例，该示例使用自定义标准和sequentialfs功能。

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.

正规化判别分析分类器

通过删除预测变量而不损害模型的预测能力，使其更加可靠，更简单。

选择Predictors for Random Forests

选择split-predictors for random forests using interaction test algorithm.

Feature Extraction

Feature Extraction

特征提取是从数据中提取高级特征的一组方法。

功能提取工作流程

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

Extract Mixed Signals

This example shows how to use里卡解开混合音频信号。

T-SNEMultidimensional Visualization

T-SNE

T-SNE是一种通过非线性还原至两个或三个维度可视化高维数据的方法，同时保留了原始数据的某些特征。

使用T-SNE可视化高维数据

此示例显示了T-SNE如何创建有用的高维数据的有用的低维嵌入。

tsneSettings

此示例显示了各种效果tsne设置。

T-SNE输出功能

Output function description and example for t-SNE.

PCA和规范相关性

Principal Component Analysis (PCA)

主成分分析通过用新的变量集替换几个相关变量来降低数据的维度，这些变量是原始变量的线性组合。

使用PCA分析美国城市的生活质量

执行加权主组件分析并解释结果。

Factor Analysis

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.

对考试成绩进行因素分析

This example shows how to perform factor analysis using Statistics and Machine Learning Toolbox™.

非负矩阵分解

非负矩阵分解

非负矩阵分解（（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.

Multidimensional Scaling

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.

经典的多维缩放

Usecmdscaleto perform classical (metric) multidimensional scaling, also known as principal coordinates analysis.

经典的多维标度应用于非空间距离

This example shows how to perform classical multidimensional scaling using thecmdscalefunction in Statistics and Machine Learning Toolbox™.

非年龄多维缩放

This example shows how to visualize dissimilarity data using nonclassical forms of multidimensional scaling (MDS).

Nonclassical and Nonmetric Multidimensional Scaling

Perform nonclassical multidimensional scaling usingmdscale。

procrustes分析

procrustes分析

Procrustes分析使用最佳形状保护的欧几里得转换最小化了比较具有里程碑意义的数据之间的位置差异。

使用Procrustes分析比较手写形状

使用Procrustes分析比较两个手写数字。

特色示例

选择ing Features for Classifying High-dimensional Data

选择ing Features for Classifying High-dimensional Data

选择用于分类高维数据的功能。更具体地说，它显示了如何执行顺序特征选择，这是最流行的特征选择算法之一。它还显示了如何使用坚持和交叉验证来评估所选功能的性能。
Open Live Script
Partial Least Squares Regression and Principal Components Regression

Partial Least Squares Regression and Principal Components Regression

应用偏最小二乘回归(PLSR)和Principal Components Regression (PCR), and discusses the effectiveness of the two methods. PLSR and PCR are both methods to model a response variable when there are a large number of predictor variables, and those predictors are highly correlated or even collinear. Both methods construct new predictor variables, known as components, as linear combinations of the original predictor variables, but they construct those components in different ways. PCR creates components to explain the observed variability in the predictor variables, without considering the response variable at all. On the other hand, PLSR does take the response variable into account, and therefore often leads to models that are able to fit the response variable with fewer components. Whether or not that ultimately translates into a more parsimonious model, in terms of its practical use, depends on the context.
Open Script
使用主组件分析拟合正交回归

使用主组件分析拟合正交回归

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.
Open Live Script

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