Generalized Embedding Regression: A Framework for Supervised Feature Extraction

Jianglin Lu, Zhihui Lai, Hailing Wang, Yudong Chen, Jie Zhou, Linlin Shen

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

Sparse discriminative projection learning has attracted much attention due to its good performance in recognition tasks. In this article, a framework called generalized embedding regression (GER) is proposed, which can simultaneously perform low-dimensional embedding and sparse projection learning in a joint objective function with a generalized orthogonal constraint. Moreover, the label information is integrated into the model to preserve the global structure of data, and a rank constraint is imposed on the regression matrix to explore the underlying correlation structure of classes. Theoretical analysis shows that GER can obtain the same or approximate solution as some related methods with special settings. By utilizing this framework as a general platform, we design a novel supervised feature extraction approach called jointly sparse embedding regression (JSER). In JSER, we construct an intrinsic graph to characterize the intraclass similarity and a penalty graph to indicate the interclass separability. Then, the penalty graph Laplacian is used as the constraint matrix in the generalized orthogonal constraint to deal with interclass marginal points. Moreover, the $L_{2,1}$ -norm is imposed on the regression terms for robustness to outliers and data's variations and the regularization term for jointly sparse projection learning, leading to interesting semantic interpretability. An effective iterative algorithm is elaborately designed to solve the optimization problem of JSER. Theoretically, we prove that the subproblem of JSER is essentially an unbalanced Procrustes problem and can be solved iteratively. The convergence of the designed algorithm is also proved. Experimental results on six well-known data sets indicate the competitive performance and latent properties of JSER.

Original languageEnglish
Pages (from-to)185-199
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume33
Issue number1
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes

Keywords

  • Generalized orthogonal constraint
  • joint sparsity
  • low-dimensional embedding
  • low-rank regression

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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