Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds

DOI10.1016/j.patcog.2010.12.015zbMath1214.62067OpenAlexW2030818161MaRDI QIDQ716365

Elnaz Barshan, Zohreh Azimifar, Mansoor Zolghadri Jahromi, Ali Ghodsi

Publication date: 28 April 2011

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2010.12.015

zbMATH Keywords

supervised learningkernel methodsdimensionality reduction

Mathematics Subject Classification ID

Related Items (11)

Reduced basis methods for time-dependent problems ⋮ Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections ⋮ Grouped feature importance and combined features effect plot ⋮ Supervised distance preserving projection using alternating direction method of multipliers ⋮ Kernel‐based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method ⋮ Optimal discriminant analysis in high-dimensional latent factor models ⋮ Shape classification by manifold learning in multiple observation spaces ⋮ Data-driven models for fault detection using kernel PCA: A water distribution system case study ⋮ Unnamed Item ⋮ Conditional t-SNE: more informative t-SNE embeddings ⋮ Supervised t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification

Uses Software

• UCI-ml

Cites Work

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• Sliced Inverse Regression for Dimension Reduction
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• Joint Measures and Cross-Covariance Operators
• Theory & Methods: Special Invited Paper: Dimension Reduction and Visualization in Discriminant Analysis (with discussion)
• Canonical Correlation Analysis: An Overview with Application to Learning Methods
• 10.1162/153244303322753742
• Learning Theory and Kernel Machines
• Algorithmic Learning Theory
• Prediction by Supervised Principal Components
• RELATIONS BETWEEN TWO SETS OF VARIATES
• Theory of Reproducing Kernels
• Comment

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