DISCRIMINANT INDEPENDENT COMPONENT ANALYSIS AS A SUBSPACE REPRESENTATION

Subspace modeling plays an important role in face recognition. Independent Component Analysis （ICA）, a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Com- ponent Analysis （PCA） technique, which addresses high order statistics as well as second order statistics. In this paper, a new scheme of subspace-based representation called Discriminant Independent Component Analysis （DICA） is proposed, which combines the strength＂ of unsupervised learning of ICA and supcrvised learning of Linear Discriminant Analysis （LDA）, and efficiently enhances the generalization ability of ICA-based representation method. Based on DICA subspace analysis, a set of optimal vectors called ＂discriminant independent faces＂ are learned from face samples. The effectiveness of our method is demonstrated by performance comparisons with some popular methods such as ICA, PCA, and PCA＋LDA. On the large scale database of IIS, significant improvements are observed when there are fewer training samples per person available.

ClustNails:Visual Analysis of Subspace Clusters

Subspace clustering addresses an important problem in clustering multi-dimensional data. In sparse multi-dimensional data, many dimensions are irrelevant and obscure the cluster boundaries. Subspace clustering helps by mining the clusters present in only locally relevant subsets of dimensions. However, understanding the result of subspace clustering by analysts is not trivial. In addition to the grouping information, relevant sets of dimensions and overlaps between groups, both in terms of dimensions and records, need to be analyzed. We introduce a visual subspace cluster analysis system called ClustNails. It integrates several novel visualization techniques with various user interaction facilities to support navigating and interpreting the result of subspace clustering. We demonstrate the effectiveness of the proposed system by applying it to the analysis of real world data and comparing it with existing visual subspace cluster analysis systems.

Subspace transform induced robust similarity measure for facial images

Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception.Nevertheless,there remains the issue of developing an efficient two-dimensional(2D)robust similarity measure method for images.Inspired by the properties of subspace,we develop an effective 2D image similarity measure technique,named transformation similarity measure(TSM),for robust face recognition.Specifically,the TSM method robustly determines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition.We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM.The performance of the TSM method,combined with the nearest neighbor rule,is evaluated in face recognition under different challenges.Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.