Abstract：Based on the canonical correlation analysis (CCA), a supervised learning algorithm, sparsity preserving CCA with missing samples (SPCCAM), is proposed. The class information of samples is introduced by sparsity preserving and cross correlation is used to overcome the limitations of the CCA and its extensions that the paired samples of different views are required. SPCCAM can combine features from different views with unpaired training samples. The experimental results on the artificial dataset, multiple feature database and PIE facial database show that the proposed SPCCAM effectively enhances the classification performance by using class information.
[1] Guillaumin M, Verbeek J, Schmid C. Multimodal Semi-Supervised Learning for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 902-909 [2] Hotelling H. Relations between Two Sets of Variates. Biometrika, 1936, 28(3/4): 321-377 [3] Devijver P A, Kittler J. Pattern Recognition: A Statistical Approach. Upper Saddle River, USA: Prentice Hall, 1982 [4] Zheng W M, Zhou X Y, Zou C R, et al. Facial Expre-ssion Recognition Using Kernel Canonical Correlation Analysis. IEEE Trans on Neural Networks, 2006, 17(1): 233-238 [5] Hardoon D R, Shawe-Taylor J. Sparse Canonical Correlation Ana-lysis. Machine Learning, 2011, 83(3): 331-353 [6] Nielsen A A. Multiset Canonical Correlations Analysis and Multispectral, Truly Multitemporal Remote Sensing Data. IEEE Trans on Image Processing, 2002, 11(3): 293-305 [7] Abraham B, Merola G. Dimensionality Reduction Approach to Multivariate Prediction. Computational Statistics & Data Analysis, 2005, 48(1): 5-16 [8] Li Y Y, Shawe-Taylor J. Using KCCA for Japanese-English Cross-Language Information Retrieval and Document Classification. Journal of Intelligent Information Systems, 2006, 27(2): 117-133 [9] Vinokourov A, Shawe-Taylor J, Cristianini J. Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis[EB/OL]. [2013-04-16]. http://machinelearning.wustl.edu/mlpapers/paper_files/AP10.pdf [10] Dempster A P, Laird N M, Rubin D B. Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 1977, 39(1): 1-38 [11] Zhang D Q, Chen S C. Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm. Neural Processing Letters, 2003, 18(3): 155-162 [12] Walczak B, Massart D. Dealing with Missing Data: Part I. Chemometrics and Intelligent Laboratory Systems, 2001, 58(1): 15-27 [13] Kim H, Golub G H, Park H. Missing Value Estimation for DNA Microarray Gene Expression Data: Local Least Squares Imputation. Bioinformatics, 2005, 21(2): 187-198 [14] Slaney M, Covell M. FaceSync: A Linear Operator for Measuring Synchronization of Video Facial Images and Audio Tracks[EB/OL].[2013-04-16]. http://www.cs.cmu.edu/Groups/NIPS/ 00papers-pub-on-web/SlaneyCovell.pdf [15] Sun T K. Research on Enhanced Canonical Correlation Analysis with Applications. Ph.D Dissertations. Nanjing, China: Nanjing University of Aeronautics and Astronautics, 2006 (in Chinese) (孙廷凯.增强型典型相关分析研究与应用.博士学位论文.南京:南京航空航天大学, 2006) [16] Chen X H, Chen S C, Xue H, et al. A Unified Dimensionality Reduction Framework for Semi-Paired and Semi-Supervised Multi-view Data. Pattern Recognition, 2012, 45(5): 2005-2018 [17] Hoegaerts L, Suykens J A K, Vandewalle J, et al. Subset Based Least Squares Subspace Regression in RKHS. Neruocomputing, 2005, 63: 293-323 [18] Sun T K, Chen S C. Locality Preserving CCA with Applications to Data Visualization and Pose Estimation. Image and Vision Computing, 2007, 25(5): 531-543 [19] Arizona State University. SLEP: Sparse Learning with Efficient Projections[DB/OL]. [2013-04-16]. http://www.public.asu.edu/~jye02/Software/SLEP [20] Zu C, Zhang D Q. Sparsity Preserving Canonical Correlation Analysis // Proc of the 5th Chinese Conference on Pattern Recognition. Beijing, China, 2012: 56-63 [21] Sun Q S, Zeng S G, Liu Y, et al. A New Method of Feature Fusion and Its Application in Image Recognition. Pattern Recognition, 2005, 38(12): 2437-2448 [22] Sun T K, Chen S C, Yang J Y, et al. Discriminative Canonical Correlation Analysis with Missing Samples // Proc of the WRI World Congress on Computer Science and Information Engineering. Los Angeles, USA, 2009: 95-99 [23] He X F, Yan S C, Hu Y X, et al. Face Recognition Using Laplacianfaces. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340 [24] Cai D, He X F, Hu Y X, et al. Learning a Spatially Smooth Subspace for Face Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007: 1-7 [25] Zhang D Q, Liu W Q. An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space // Proc of the 21st International Joint Conference on Artificial Intelligence. Pasadena, USA, 2009: 1345-1350 [26] Zhang D Q, Zhou Z H, Chen S C. Non-Negative Matrix Factorization on Kernels // Proc of the 9th Pacific Rim International Conference on Artificial Intelligence. Guilin, China, 2006: 404-412
2014, Vol. 27 
