Dual Pursuit for Subspace Learning

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Dual Pursuit for Subspace Learning

Shuangyan Yi; Yingyi Liang; Zhenyu He; Yi Li; Yiu-Ming Cheung

In general, low-rank representation (LRR) aims to find the lowest rank representation with respect to a dictionary. In fact, the dictionary is a key aspect of low-rank representation. However, a lot of low-rank representation methods usually use the data itself as a dictionary (i.e., a fixed dictionary), which may degrade their performances due to the lack of clustering ability of a fixed dictionary. To this end, we propose learning a locality-preserving dictionary instead of the fixed dictionary for low-rank representation, where the locality-preserving dictionary is constructed by using a graph regularization technique to capture the intrinsic geometric structure of the dictionary and, hence, the locality-preserving dictionary has an underlying clustering ability. In this way, the obtained low-rank representation via the locality-preserving dictionary has a better grouping-effect representation. Inversely, a better grouping-effect representation can help to learn a good dictionary. The locality-preserving dictionary and the grouping-effect representation interact with each other, where dual pursuit is called. The proposed method, namely, Dual Pursuit for Subspace Learning, provides us with a robust method for clustering and classification simultaneously, and compares favorably with the other state-of-the-art methods.

SPS on Facebook

SPS on Twitter

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar