Abstract
Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on. This paper investigates the problem of robust face classification when a test sample has missing values. Firstly, we propose a classification method based on the incomplete sparse representation. This representation is boiled down to an l1 minimization problem and an alternating direction method of multipliers is employed to solve it. Then, we provide a convergent analysis and a model extension on incomplete sparse representation. Finally, we conduct experiments on two real-world face datasets and compare the proposed method with the nearest neighbor classifier and the sparse representation-based classification. The experimental results demonstrate that the proposed method has the superiority in classification accuracy, completion of the missing entries and recovery of noise.
Highlights
As a parsimony method, the sparse signal representation means that we desire to represent a signal by the linear combination of a few basis elements in an over-complete dictionary
We propose a method of incomplete sparse representation for classification
This paper studies the problem of robust face classification with incomplete test samples
Summary
The sparse signal representation means that we desire to represent a signal by the linear combination of a few basis elements in an over-complete dictionary. The sparse representation has two powerful functions, that is, it is robust to large sparse corruptions and discriminative to class labels These two distinguished functions promote its extensive and successful applications in areas such as pattern classification [3,4,5], computer vision [6] and machine learning [7]. It is worth mentioning that Wright et al [3] proposed a novel method for robust face classification. If we make a further clustering analysis on these datasets, Shi et al [14] proposed the method of incomplete low-rank representation, which is validated to be very robust to missing values. This paper considers the pattern classification problem that the test samples have missing values while the training samples are complete.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
