Name
Abstract | ||
|---|---|---|
We address the following subspace learning problem: supposing we are given a set of labeled, corrupted training data points, how to learn the underlying subspace, which contains three components: an intrinsic subspace that captures certain desired properties of a data set, a penalty subspace that fits the undesired properties of the data, and an error container that models the gross corruptions possibly existing in the data. Given a set of data points, these three components can be learned by solving a nuclear norm regularized optimization problem, which is convex and can be efficiently solved in polynomial time. Using the method as a tool, we propose a new discriminant analysis (i.e., supervised subspace learning) algorithm called Corruptions Tolerant Discriminant Analysis (CTDA), in which the intrinsic subspace is used to capture the features with high within-class similarity, the penalty subspace takes the role of modeling the undesired features with high between-class similarity, and the error container takes charge of fitting the possible corruptions in the data. We show that CTDA can well handle the gross corruptions possibly existing in the training data, whereas previous linear discriminant analysis algorithms arguably fail in such a setting. Extensive experiments conducted on two benchmark human face data sets and one object recognition data set show that CTDA outperforms the related algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TIP.2013.2273665 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
graph embedding,supervised subspace learning algorithm,face recognition,discriminant analysis,object recognition data set,learning (artificial intelligence),ctda,error container,polynomial time,convex programming,general subspace learning,intrinsic subspace,penalty subspace,subspace learning,benchmark human face data sets,computational complexity,nuclear norm regularized optimization problem,object recognition,corruptions tolerant discriminant analysis,gross corruption modelling,graph theory,labeled corrupted training data points,within-class similarity,between-class similarity,corrupted training data,convex optimization,learning artificial intelligence | Data point,Computer vision,Facial recognition system,Data set,Subspace topology,Pattern recognition,Graph embedding,Artificial intelligence,Linear discriminant analysis,Optimization problem,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
22 | 11 | 1941-0042 |
Citations | PageRank | References |
19 | 0.65 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing-Kun Bao | 1 | 283 | 18.82 |
| Guangcan Liu | 2 | 2515 | 76.85 |
| Richang Hong | 3 | 4791 | 176.47 |
| Shuicheng Yan | 4 | 767 | 25.71 |
| Changsheng Xu | 5 | 4957 | 332.87 |