Robust Latent Subspace Learning for Image Classification

Xiaozhao Fang, Shaohua Teng, Zhihui Lai, Zhaoshui He, Shengli Xie, Wai Keung Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)

Abstract

This paper proposes a novel method, called robust latent subspace learning (RLSL), for image classification. We formulate an RLSL problem as a joint optimization problem over both the latent SL and classification model parameter predication, which simultaneously minimizes: 1) the regression loss between the learned data representation and objective outputs and 2) the reconstruction error between the learned data representation and original inputs. The latent subspace can be used as a bridge that is expected to seamlessly connect the origin visual features and their class labels and hence improve the overall prediction performance. RLSL combines feature learning with classification so that the learned data representation in the latent subspace is more discriminative for classification. To learn a robust latent subspace, we use a sparse item to compensate error, which helps suppress the interference of noise via weakening its response during regression. An efficient optimization algorithm is designed to solve the proposed optimization problem. To validate the effectiveness of the proposed RLSL method, we conduct experiments on diverse databases and encouraging recognition results are achieved compared with many state-of-the-arts methods.
Original languageEnglish
Pages (from-to)2502-2515
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number6
DOIs
Publication statusPublished - 1 Jun 2018

Keywords

  • Classification
  • computer vision
  • data representation
  • linear regression (LR)
  • subspace learning (SL)

ASJC Scopus subject areas

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

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