DocumentCode :
2957382
Title :
Latent Low-Rank Representation for subspace segmentation and feature extraction
Author :
Liu, Guangcan ; Yan, Shuicheng
Author_Institution :
Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
fYear :
2011
fDate :
6-13 Nov. 2011
Firstpage :
1615
Lastpage :
1622
Abstract :
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dictionary, which is a key aspect of LRR. However, such a strategy may depress the performance, especially when the observations are insufficient and/or grossly corrupted. In this paper we therefore propose to construct the dictionary by using both observed and unobserved, hidden data. We show that the effects of the hidden data can be approximately recovered by solving a nuclear norm minimization problem, which is convex and can be solved efficiently. The formulation of the proposed method, called Latent Low-Rank Representation (LatLRR), seamlessly integrates subspace segmentation and feature extraction into a unified framework, and thus provides us with a solution for both subspace segmentation and feature extraction. As a subspace segmentation algorithm, LatLRR is an enhanced version of LRR and outperforms the state-of-the-art algorithms. Being an unsupervised feature extraction algorithm, LatLRR is able to robustly extract salient features from corrupted data, and thus can work much better than the benchmark that utilizes the original data vectors as features for classification. Compared to dimension reduction based methods, LatLRR is more robust to noise.
Keywords :
convex programming; data encapsulation; data structures; feature extraction; image segmentation; matrix algebra; minimisation; vectors; LatLRR; convex problem; corrupted data; data vectors; dictionary; dimension reduction based methods; hidden data; latent low-rank representation; multiple subspace data structures; nuclear norm minimization problem; observed data matrix; salient features; state-of-the-art algorithms; subspace segmentation algorithm; unsupervised feature extraction algorithm; Dictionaries; Feature extraction; Motion segmentation; Noise; Robustness; Strontium; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2011 IEEE International Conference on
Conference_Location :
Barcelona
ISSN :
1550-5499
Print_ISBN :
978-1-4577-1101-5
Type :
conf
DOI :
10.1109/ICCV.2011.6126422
Filename :
6126422
Link To Document :
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