Localized Sliced Inverse Regression
We developed localized sliced inverse regression for supervised dimension reduction. It has the advantages of preventing degeneracy, increasing estimation accuracy, and automatic subclass discovery in classification problems. A semisupervised version is proposed for the use of unlabeled data. The utility is illustrated on simulated as well as real data sets.
Code (0)
등록된 구현이 없습니다.
Tasks
Dimensionality ReductionGeneral ClassificationregressionSimilar Papers 제목 키워드 기반
Randomized Dimension Reduction on Massive Data
Scalability of statistical estimators is of increasing importance in modern applications and dimension reduction is often used to extract relevant information from data. A variety of popular dimension reduction approache…
Dimensionality ReductionregressionOnline Kernel Sliced Inverse Regression
Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other me…
Dimensionality ReductionregressionStochastic OptimizationOverlapping Sliced Inverse Regression for Dimension Reduction
Sliced inverse regression (SIR) is a pioneer tool for supervised dimension reduction. It identifies the effective dimension reduction space, the subspace of significant factors with intrinsic lower dimensionality. In thi…
Dimensionality ReductionregressionA convex formulation for high-dimensional sparse sliced inverse regression
Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the…
Dimensionality ReductionregressionVariable SelectionVocal Bursts Intensity PredictionDifferentially private sliced inverse regression in the federated paradigm
Sliced inverse regression (SIR), which includes linear discriminant analysis (LDA) as a special case, is a popular and powerful dimension reduction tool. In this article, we extend SIR to address the challenges of decent…
Dimensionality Reductionregression