Class-Discriminative Kernel Sparse Representation-Based Classification Using Multi-Objective Optimization
In this paper, we propose class-discriminative kernel sparse representation-based classification (KSRC) using multi-objective optimization (MOO) called KSRC 2.0.In sparse representation-based classification (SRC), both dictionary and residuals (reconstruction errors) play an important role in classifying a sample.Thus, discriminative dictionary and residuals are required to achieve high classification performance.To generate discriminative dictionary and residuals from training data sets, we formulate multi-objective functions via the Fisher discrimination criterion that minimizes distances within and maximizes distances between classes.Then, we solve them by using MOO, which can optimize conflicting objectives at the same time, and obtain component importance factors to make dictionary and residuals class-discriminative.Extensive experiments on publicly available databases demonstrate that the proposed KSRC 2.0 enhances the class separability of KSRC and achieves high classification performance.
KSRC 2.0 class-discriminative dictionary learning image classification multi-objective optimization sparse representation
Meng Jian Cheolkon Jung
IEEE
国内会议
广州
英文
197-208
2015-05-01（万方平台首次上网日期，不代表论文的发表时间）