20161026-36 朱鹏飞：Feature oriented Self-representation and its applications .. ...

报告嘉宾2：朱鹏飞 （天津大学）

报告时间：2016年10月26日（星期三）晚21:00（北京时间）

报告题目：Feature oriented Self-representation and its applications 

主持人：任传贤（中山大学）

Almost all the existing representation based classifiers represent a query sample as a linear combination of training samples, and their time and memory cost will increase rapidly with the number of training samples. We investigate the representation based classification problem from a rather different perspective in this paper, that is, we learn how each feature (i.e., each element) of a sample can be represented by the features of itself. Such a self-representation property of sample features can be readily employed for pattern classification and a novel self-representation induced classifier (SRIC) is proposed. SRIC learns a self-representation matrix for each class. Given a query sample, its self-representation residual can be computed by each of the learned self-representation matrices, and classification can then be performed by comparing these residuals. In light of the principle of SRIC, a discriminative SRIC (DSRIC) method is developed. For each class, a discriminative self-representation matrix is trained to minimize the self-representation residual of this class while representing little the features of other classes. Experimental results on different pattern recognition tasks show that DSRIC achieves comparable or superior recognition rate to state-of-the-art representation based classifiers, however, it is much more efficient and needs much less storage space.

VOOC责任委员：任传贤（中山大学）

VODB协调理事：张利军（南京大学），章国锋（浙江大学）