Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79215
Title: Robust latent subspace learning for image classification
Authors: Fang, XZ
Teng, SH
Lai, ZH
He, ZS
Xie, SL
Wong, WK 
Keywords: Classification
Computer vision
Data representation
Linear regression (LR)
Subspace learning (SL)
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, June 2018, v. 29, no. 6, p. 2502-2515 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
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.
URI: http://hdl.handle.net/10397/79215
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2017.2693221
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