Pattern Recognition: Chinese Conference, CCPR 2012, Beijing, by Yuting Tao, Jian Yang (auth.), Cheng-Lin Liu, Changshui

By Yuting Tao, Jian Yang (auth.), Cheng-Lin Liu, Changshui Zhang, Liang Wang (eds.)

This booklet constitutes the refereed lawsuits of the chinese language convention on trend attractiveness, CCPR 2012, held in Beijing, China, in September 2012. The eighty two revised complete papers offered have been rigorously reviewed and chosen from 137 submissions. The papers are prepared in topical sections on development acceptance concept; laptop imaginative and prescient; biometric popularity; scientific imaging; photograph and video research; rfile research; speech processing; normal language processing and data retrieval.

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The global and neighborhood structures information are modeled as a graph regularized least squares problem. We design an efficient model selection scheme for the optimal parameter estimation, which balances the tradeoff between the global and neighborhood structures. Extensive experimental studies are conducted on benchmark data sets to show the effectiveness of our approach. Keywords: linear regression, dimensionality reduction, geometric structure, model selection. 1 Introduction Dimensionality reduction has attracted tremendous attention in computer vision, statistical learning and pattern recognition [1,2].

Local knowledge fuse conduct Expert experiences ... decompose .... + + construct + Data + extract BNs with CPT _ ? QPN with conflict + combine + + + _ + + Conflict resolution Fig. 1. The framework of qualitative graphical inference with knowledge fusion From the above, whether the qualitative and quantitative integration methods or the network decomposition and synthesis methods, the essential framework of conflict resolution as shown in figure1, is a kind of further explanation of the problem domain and combination of global information and local knowledge.

Among them principal component analysis(PCA) [3] and linear discriminant analysis(LDA) [1,4] are two most popular and well-studied methods. PCA aims to find a set of orthogonal basis that capture the directions of maximum variance of the data set. Unlike PCA which is unsupervised, LDA is supervised. LDA aims to find optimal basis by maximizing the between-class distance while minimizing the within-class distance simultaneously. LDA has been applied successfully to many areas such as face recognition [5,6], document classification [7] and microarray data analysis [8].

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