Robust Latent Subspace Learning for Image Classification

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 6; pp. 2502 - 2515
• Main Authors: Fang, Xiaozhao, Teng, Shaohua, Lai, Zhihui, He, Zhaoshui, Xie, Shengli, Wong, Wai Keung
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Classification; computer vision; Data models; Data processing; data representation; Error compensation; Image classification; Learning; linear regression (LR); Optimization; Principal component analysis; Representations; Robustness; subspace learning (SL); Subspace methods; Subspaces; Training; Visualization
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2017.2693221

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.