SF-KCCA: Sample Factoring Induced Kernel Canonical Correlation Analysis

The Canonical Correlation analysis (CCA), such as linear CCA and Kernel Canonical Correlation Analysis (KCCA) are efficient methods for dimensionality reduction (DR). In this paper, a method of sample factoring induced KCCA is proposed. Different from traditional KCCA method, sample factors are introduced to impose penalties on the sample spaces to suppress the effect of corrupt data samples. By using a sample factoring strategies: cosine similarity metrics, the relationships between data samples and the principal projections are iteratively learned in order to obtain better correlation projections. By this way, the authentic and corrupt data samples can be discriminated and the impact of the corrupt data samples can be suppressed. Extensive experiments conducted on face image datasets, such as Yale, AR, show our approach has better classification and DR performance than that of linear CCA and KCCA, especially in noisy datasets.

[1]  Daoqiang Zhang,et al.  A New Locality-Preserving Canonical Correlation Analysis Algorithm for Multi-View Dimensionality Reduction , 2013, Neural Processing Letters.

[2]  Hong Liu,et al.  Linear canonical correlation analysis based ranking approach for facial age estimation , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[3]  Seungjin Choi,et al.  Two-Dimensional Canonical Correlation Analysis , 2007, IEEE Signal Processing Letters.

[4]  Ignacio Santamaría,et al.  Blind Identification of SIMO Wiener Systems Based on Kernel Canonical Correlation Analysis , 2013, IEEE Transactions on Signal Processing.

[5]  Gongxuan Zhang,et al.  ApproxCCA: An approximate correlation analysis algorithm for multidimensional data streams , 2011, Knowl. Based Syst..

[6]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[7]  Xiaoyuan Jing,et al.  Multiple kernel ensemble learning for software defect prediction , 2015, Automated Software Engineering.

[8]  Raman Arora,et al.  Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Daoqiang Zhang,et al.  Multi-view dimensionality reduction via canonical random correlation analysis , 2015, Frontiers of Computer Science.

[10]  A. Tenenhaus,et al.  Regularized Generalized Canonical Correlation Analysis , 2011, Eur. J. Oper. Res..

[11]  W. Zheng,et al.  Facial expression recognition using kernel canonical correlation analysis (KCCA) , 2006, IEEE Transactions on Neural Networks.

[12]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[13]  K. Fukumizu,et al.  Sensitivity analysis in robust and kernel canonical correlation analysis , 2008, 2008 11th International Conference on Computer and Information Technology.

[14]  Baowen Xu,et al.  Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction , 2018, Automated Software Engineering.

[15]  Javier Redondo,et al.  Massive hidden photons as lukewarm dark matter , 2008, 0811.0326.

[16]  Catherine Dehon,et al.  Robust Methods for Canonical Correlation Analysis , 2000 .

[17]  Quan-Sen Sun,et al.  Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition , 2014, Pattern Recognit..

[18]  Songcan Chen,et al.  Locality preserving CCA with applications to data visualization and pose estimation , 2007, Image Vis. Comput..

[19]  L. Liao,et al.  Sparse Kernel Canonical Correlation Analysis via $\ell_1$-regularization , 2017, 1701.04207.

[20]  Jiwen Lu,et al.  Localized multi-kernel discriminative canonical correlation analysis for video-based person re-identification , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[21]  Albert Cohen,et al.  Breaking the curse of dimensionality in sparse polynomial approximation of parametric PDEs , 2015 .