Abstract
Canonical correlation analysis (CCA) and partial least squares (PLS) are always used as fusing two feature sets. How to extend them to fuse multiple features in a generalized way is still an unsolved problem. In this paper, we propose a novel feature fusion method called multiple component analysis (MCA). By constructing a higher-order tensor, all kinds of information are fused into the covariance tensor. Then orthogonal subspaces corresponding to each feature set are learned through tensor singular value decomposition (SVD), that couples dimension reduction and feature fusion together. Compared with multiple feature fusion by subspace learning (MFFSL), our method has the ability to represent fused data more efficiently and discriminatively in very few components. And it is shown that principle component analysis (PCA) and PLS are special cases of our method when there are only one set and two sets of features respectively. Extensive experiments on both handwritten numerals classification and face recognition demonstrate the effectiveness and robustness of the proposed method.
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References
Liu M, Fu Y, Huang TS (2008) An audio-visual fusion framework with joint dimensionality reduction. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP)
Kuncheva LI (2004) Combining pattern classification, methods and algorithms. Wiley, New York
Cetingül HE, Erzin E, Yemez Y, Tekalp AM (2008) Multimodal speaker/speech recognition using lip motion, lip texture and audio. Signal Process 86(2): 3549–3558
Ai L, Wang J, Wang X (2008) Multi-feature fusion diagnosis of tremor based on artificial neural network and D-S evidence theory. Signal Process 88(12): 2927–2935
Liu Z, Liu C (2010) Fusion of color, local spatial and global frequency information for face recognition. Pattern Recognit 43(8): 2882–2890
Nadal C, Legault R, Suen CY (1990) Complementary algorithms for the recognition of totally unconstrained handwritten numerals. In: Proceedings of the 10th international conference on pattern recognition (ICPR)
Nanni L (2005) Fusion of classifiers for protein fold recognition. Neurocomputing 68: 315–321
Lanckriet GRG, Cristianini N, Bartlett P, El Ghaoui L, Jordan MI (2004) Learning the kernel matrix with semidefinite programming. J Mach Learn Res 5: 27–72
Rakotomamonjy A, Bach FR, Canu S, Grandvalet Y (2008) SimpleMKL. J Mach Learn Res 9: 249–252
Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: Proceedings of IEEE international conference on computer vision (ICCV)
Muslea I, Minton S, Knoblock CA (2006) Active learning with multiple views. J Artif Intell Res 27(1): 203–233
Long B, Yu PS, Zhang Z (2008) A general model for multiple view unsupervised learning. In: Proceedings of 8th SIAM international conference on data mining (SDM)
Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybernet B 40(6): 1438–1446
Sun B, Zhang X, Liu J, Mao X (2010) Feature fusion using locally linear embedding for classification. IEEE Trans Neural Netw 21(1): 163–168
Hou C, Zhang C, Wu Y, Nie F (2010) Multiple view semi-supervised dimension reduction. Pattern Recognit 43(3): 720–730
Yun F, Cao L, Guo G, Huang TS (2008) Multiple feature fusion by subspace learning. In: Proceedings of ACM international conference on image and video retrieval (CIVR)
Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang H (2007) Multilinear discriminant analysis for face recognition. IEEE Trans Image Process. 16(1): 212–220
Tao D, Li X, Wu X, Maybank SJ (2007) General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10): 1700–1714
Lu H, Plataniotis KN, Venesanopoulos AN (2009) Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition. IEEE Trans Neural Netw 20(1): 103–123
Lu H, Plataniotis KN, Venesanopoulos AN (2009) Uncorrelated multilinear principal component analysis for unsupervised multilinear subsapce learning. IEEE Trans Neural Netw 20(11): 1820–1836
Wang H, Ahuja N (2008) A tensor approximation approach to dimensionality reduction. Int J Comput Vis 76: 217–229
Wang S-J, Yang J, Zhang N, Zhou C-G (2011) Tensor discriminant color space for face recognition. IEEE Trans Image Process. doi:10.1109/TIP.2011.2121084
Alex M, Vasilescu O, Terzopoulos D (2002) Multilinear analysis of image ensembles: TensorFaces. In: Proceedings of the European conference on computer vision (ECCV)
Lathauwer L, Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4): 1253–1278
Kim YD, Choi S (2007) Color face tensor factorization and slicing for illumination robust recognition. In: Proceedings of the international conference on biometrics
De Lathauwe L, De Moor B, Vandewalle J (1994) Blind source separation by higher-order singular value decomposition. In: Proceedings of signal processing VII: theories and applications (EUSIPCO-94), Edinburgh, UK
Sun QS, Jin Z, Heng PA, Xia DS (2005) A novel feature fusion method based on partial least squares regression. In: Proceedings of the third international conference on advances in pattern recognition. Lecture notes in computer science. Springer, Bath
Sun L, Ji S, Yu S, Ye J (2009) On the equivalence between canonical correlation analysis and orthonormalized partial least squares. In: IJCAI, pp 1230–1235
Bader BW, Kolda TG (2010) MATLAB tensor toolbox, version 2.4. http://csmr.ca.sandia.gov/~tgkolda/TensorToolbox/
Hu ZS, Lou Z, Yang JY, Liu K, Suen CY (1999) Handwrittern digital recognition basis on multi-classifier combination. Chin J Comput 22(1): 369–374
Lee TS (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10): 959–971
Liao SX, Pawlak M (1996) On image analysis by moments. IEEE Trans Pattern Anal Mach Intell 18(3): 254–266
Bailey RR, Mandyam S (1996) Orthogonal moment feature for use with parametric and non-parametric classifiers. IEEE Trans Pattern Anal Mach Intell 18(4): 389–398
Alieza K, Yawhua H (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5): 489–497
Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10): 1090–1104
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7): 971–987
Ahonen T, Hadid A, Pietikainen M (2006) Face description with binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12): 2037–2041
Yoshihiko H et al (1996) Recognition of handwriting numerals using Gabor features. In: Proceedings of the thirteenth international conference on pattern recognition (ICPR)
Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional cortical filters. J Opt Soc Am 2(7): 1160–1169
Tenllado C, Gomez JI, Setoain J, Mora D, Prieto M (2010) Improving face recognition by combination of natural and Gabor faces. Pattern Recognit Lett 31(11): 1453–1460
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Hou, S., Sun, Q. & Xia, D. Feature Fusion Using Multiple Component Analysis. Neural Process Lett 34, 259–275 (2011). https://doi.org/10.1007/s11063-011-9197-6
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DOI: https://doi.org/10.1007/s11063-011-9197-6