Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2011.6126422
Title: Latent low-rank representation for subspace segmentation and feature extraction
Authors: Liu, G. 
Yan, S. 
Issue Date: 2011
Source: Liu, G.,Yan, S. (2011). Latent low-rank representation for subspace segmentation and feature extraction. Proceedings of the IEEE International Conference on Computer Vision : 1615-1622. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2011.6126422
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. © 2011 IEEE.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/70771
ISBN: 9781457711015
DOI: 10.1109/ICCV.2011.6126422
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