An enhanced sparse representation strategy for signal classification

Yin Zhou; Jinglun Gao; Kenneth E. Barner

doi:10.1117/12.919469

8 June 2012 An enhanced sparse representation strategy for signal classification

Yin Zhou, Jinglun Gao, Kenneth E. Barner

Proceedings Volume 8365, Compressive Sensing; 83650H (2012) https://doi.org/10.1117/12.919469
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Sparse representation based classification (SRC) has achieved state-of-the-art results on face recognition. It is hence desired to extend its power to a broader range of classification tasks in pattern recognition. SRC first encodes a query sample as a linear combination of a few atoms from a predefined dictionary. It then identifies the label by evaluating which class results in the minimum reconstruction error. The effectiveness of SRC is limited by an important assumption that data points from different classes are not distributed along the same radius direction. Otherwise, this approach will lose their discrimination ability, even though data from different classes are actually well-separated in terms of Euclidean distance. This assumption is reasonable for face recognition as images of the same subject under different intensity levels are still considered to be of same-class. However, the assumption is not always satisfied when dealing with many other real-world data, e.g., the Iris dataset, where classes are stratified along the radius direction. In this paper, we propose a new coding strategy, called Nearest- Farthest Neighbors based SRC (NF-SRC), to effectively overcome the limitation within SRC. The dictionary is composed of both the Nearest Neighbors and the Farthest Neighbors. While the Nearest Neighbors are used to narrow the selection of candidate samples, the Farthest Neighbors are employed to make the dictionary more redundant. NF-SRC encodes each query signal in a greedy way similar to OMP. The proposed approach is evaluated over extensive experiments. The encouraging results demonstrate the feasibility of the proposed method.

Citation Download Citation

Yin Zhou, Jinglun Gao, and Kenneth E. Barner "An enhanced sparse representation strategy for signal classification", Proc. SPIE 8365, Compressive Sensing, 83650H (8 June 2012); https://doi.org/10.1117/12.919469

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