# American Institute of Mathematical Sciences

August  2020, 19(8): 3933-3945. doi: 10.3934/cpaa.2020173

## Sparse generalized canonical correlation analysis via linearized Bregman method

 1 School of Statistics and Mathematics, Collaborative Innovation Development Center of, Pearl River Delta Science & Technology Finance Industry, Guangdong University of Finance & Economics, Guangzhou, Guangdong, 510320, China 2 School of Electronics and Computer Science, University of Southampton, University Road, Southampton, SO17 1BJ, United Kingdom

* Corresponding author

Received  February 2019 Revised  August 2019 Published  May 2020

Fund Project: The work described in this paper is supported partially by National Natural Science Foundation of China (11871167,11671171), Science and Technology Program of Guangzhou (201707010228), Project of Collaborative Innovation Development Center of Pearl River Delta Science & Technology Finance Industry (19XT01), National Social Science Foundation (19AJY027), Guangdong Province Innovative Team Program (2017WCXTD004), Special Support Plan for High-Level Talents of Guangdong Province (2019TQ05X571), Foundation of Guangdong Educational Committee (2019KZDZX1023)

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting mutual information between two sets of multi-dimensional random variables. Unlike CCA, Generalized CCA (GCCA), a natural extension of CCA, could detect the relations of multiple datasets (more than two). To interpret canonical variates more efficiently, this paper addresses a novel sparse GCCA algorithm via linearized Bregman method, which is a generalization of traditional sparse CCA methods. Experimental results on both synthetic dataset and real datasets demonstrate the effectiveness and efficiency of the proposed algorithm when compared with several state-of-the-art sparse CCA and deep CCA algorithms.

Citation: Jia Cai, Junyi Huo. Sparse generalized canonical correlation analysis via linearized Bregman method. Communications on Pure and Applied Analysis, 2020, 19 (8) : 3933-3945. doi: 10.3934/cpaa.2020173
##### References:
 [1] G. Andrew, R. Arora, J. Bilmes and K. Livescu, Deep canonical correlation analysis, in International Conference on Machine Learning, (2013), 1247–1255. [2] A. Benton, R. Arora and and M. Dredze, Learning multiview embeddings of twitter users, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2 (2016), 14-19. [3] A. Benton, H. Khayrallah, B. Gujral and et al., Deep Generalized Canonical Correlation Analysis, in Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), (2019), 1–6. [4] L. M. Br $\grave{e}$ gman, A relaxation method of finding a common point of convex sets and its application to the solution of problems in convex programming, Zh. Vychisl. Mat. Mat. Fiz., 7 (1967), 620-631. [5] J. F. Cai, S. Osher and Z. Shen, Convergence of the linearized bregman iteration for $\ell_1$-norm minimization, Math. Comp., 78 (2009), 2127-2136.  doi: 10.1090/S0025-5718-09-02242-X. [6] J. F. Cai, S. Osher and Z. Shen, Linearized bregman iterations for compressed sensing, Math. Comp., 78 (2009), 1515-1536.  doi: 10.1090/S0025-5718-08-02189-3. [7] J. Carroll, Equations and tables for a generalization of canonical correlation analysis to three or more sets of variables, Proceedings of Annual Convention of The American Psychological Association, 3 (1968), 227-228. [8] M. Chen, C. Gao, Z. Ren and et al., Sparse cca via precision adjusted iterative thresholding, in Proceedings of International Congress of Chinese Mathematicians, (2016). [9] D. Chu, L. Z. Liao, M. K. Ng and X. W. Zhang, Sparse canonical correlation analysis: new formulation and algorithm, IEEE Trans. Pattern Anal. Mach. Intell., 35 (2013), 3050-3065.  doi: 10.1109/TPAMI.2013.104. [10] M. Dettling, Bagboosting for tumor classification with gene expression data, Bioinformatics, 20 (2004), 3583-3593.  doi: 10.1093/bioinformatics/bth447. [11] O. Friman, J. Cedefamn, P. Lundberg, H. Borga and H. Knutsson, Detection of neural activity in functional mri using canonical correlation analysis, Magn. Reson. Med., 45 (2001), 323-330.  doi: 10.1002/1522-2594(200102)45:2<323::aid-mrm1041>3.0.co;2-#. [12] C. Gao, Z. Ma and H. H. Zhou, Sparse cca: Adaptive estimation and computational barriers, Ann. Statist., 45 (2017), 2074-2101.  doi: 10.1214/16-AOS1519. [13] D. R. Hardoon and J. Shawe-Taylor, Sparse canonical correlation analysis, Mach. Learn., 83 (2011), 331-353.  doi: 10.1007/s10994-010-5222-7. [14] D. R. Hardoon, S. Szedmak and and J. Shawe-Taylor, Canonical correlation analysis: An overview with application to learning methods, Neural Comput., 16 (2004), 2639-2664.  doi: 10.1162/0899766042321814. [15] P. Horst, Generalized canonical correlations and their applications to experimental data, J. Clin. Psychol., 17 (1961), 331-347. [16] H. Hotelling, Relations between two sets of variates, Biometrika, 2 (1936), 321-377. [17] M. Kang, B. Zhang, X. Wu, C. Y. Liu and J. Gao, Sparse generalized canonical correlation analysis for biological model integration: a genetic study of psychiatric disorders, in 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2013), 1490–1493. [18] J. R. Kettenring, Canonical analysis of several sets of variables, Biometrika, 58 (1971), 433-451.  doi: 10.1093/biomet/58.3.433. [19] Y. Luo, D. Tao, K. Ramamohanarao, C. Xu and Y. G. Wen, Tensor canonical correlation analysis for multi-view dimension reduction, IEEE Trans. Knowl. Data Eng., 27 (2015), 3111-3124.  doi: 10.1109/TKDE.2015.2445757. [20] S. Osher, M. Burger, D. Goldfarb, J. J. Xu and W. T. Ying, An iterative regularization method for total variation-based image restoration, Multiscale Model. Simul., 4 (2005), 460-489.  doi: 10.1137/040605412. [21] R. Steinberger, B. Pouliquen, A. Widiger and et al., The jrc-acquis: A multilingual aligned parallel corpus with 20+ languages, in Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC$\prime$ 2006), (2006), 2142–2147. [22] J. V$\acute{I}$a, I. Santamar$\acute{I}$a and and J. P$\acute{e}$rez, A learning algorithm for adaptive canonical correlation analysis of several data sets, Neural Netw., 20 (2007), 139-152.  doi: 10.1016/j.neunet.2006.09.011. [23] A. Vinokourov, N. Cristianini and J. Shawe-Taylor, Inferring a semantic representation of text via cross-language correlation analysis, in Advances in Neural Information Processing Systems, (2003), 1497–1504. [24] S. Waaijenborg, P. C. V. de Witt Hamer and A. H. Zwinderman, Quantifying the association between gene expressions and dna-markers by penalized canonical correlation analysis, Stat. Appl. Genet. Mol. Biol., 7 (2008), Art. 3. doi: 10.2202/1544-6115.1329. [25] D. M. Witten, R. Tibshirani and T. Hastie, A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis, Biostatistics, 10 (2009), 515-534.  doi: 10.1093/biostatistics/kxp008. [26] Y. Yamanishi, J. P. Vert, A. Nakaya and M. Kanehisa, Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis, Bioinformatics, 19 (2003), i323–i330. doi: 10.1093/bioinformatics/btg1045. [27] W. Yin, S. Osher, D. Goldfarb and J. Darbon, Bregman iterative algorithms for 1-minimization with applications to compressed sensing, SIAM J. Imaging Sci., 1 (2008), 143-168.  doi: 10.1137/070703983.

show all references

##### References:
 [1] G. Andrew, R. Arora, J. Bilmes and K. Livescu, Deep canonical correlation analysis, in International Conference on Machine Learning, (2013), 1247–1255. [2] A. Benton, R. Arora and and M. Dredze, Learning multiview embeddings of twitter users, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2 (2016), 14-19. [3] A. Benton, H. Khayrallah, B. Gujral and et al., Deep Generalized Canonical Correlation Analysis, in Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), (2019), 1–6. [4] L. M. Br $\grave{e}$ gman, A relaxation method of finding a common point of convex sets and its application to the solution of problems in convex programming, Zh. Vychisl. Mat. Mat. Fiz., 7 (1967), 620-631. [5] J. F. Cai, S. Osher and Z. Shen, Convergence of the linearized bregman iteration for $\ell_1$-norm minimization, Math. Comp., 78 (2009), 2127-2136.  doi: 10.1090/S0025-5718-09-02242-X. [6] J. F. Cai, S. Osher and Z. Shen, Linearized bregman iterations for compressed sensing, Math. Comp., 78 (2009), 1515-1536.  doi: 10.1090/S0025-5718-08-02189-3. [7] J. Carroll, Equations and tables for a generalization of canonical correlation analysis to three or more sets of variables, Proceedings of Annual Convention of The American Psychological Association, 3 (1968), 227-228. [8] M. Chen, C. Gao, Z. Ren and et al., Sparse cca via precision adjusted iterative thresholding, in Proceedings of International Congress of Chinese Mathematicians, (2016). [9] D. Chu, L. Z. Liao, M. K. Ng and X. W. Zhang, Sparse canonical correlation analysis: new formulation and algorithm, IEEE Trans. Pattern Anal. Mach. Intell., 35 (2013), 3050-3065.  doi: 10.1109/TPAMI.2013.104. [10] M. Dettling, Bagboosting for tumor classification with gene expression data, Bioinformatics, 20 (2004), 3583-3593.  doi: 10.1093/bioinformatics/bth447. [11] O. Friman, J. Cedefamn, P. Lundberg, H. Borga and H. Knutsson, Detection of neural activity in functional mri using canonical correlation analysis, Magn. Reson. Med., 45 (2001), 323-330.  doi: 10.1002/1522-2594(200102)45:2<323::aid-mrm1041>3.0.co;2-#. [12] C. Gao, Z. Ma and H. H. Zhou, Sparse cca: Adaptive estimation and computational barriers, Ann. Statist., 45 (2017), 2074-2101.  doi: 10.1214/16-AOS1519. [13] D. R. Hardoon and J. Shawe-Taylor, Sparse canonical correlation analysis, Mach. Learn., 83 (2011), 331-353.  doi: 10.1007/s10994-010-5222-7. [14] D. R. Hardoon, S. Szedmak and and J. Shawe-Taylor, Canonical correlation analysis: An overview with application to learning methods, Neural Comput., 16 (2004), 2639-2664.  doi: 10.1162/0899766042321814. [15] P. Horst, Generalized canonical correlations and their applications to experimental data, J. Clin. Psychol., 17 (1961), 331-347. [16] H. Hotelling, Relations between two sets of variates, Biometrika, 2 (1936), 321-377. [17] M. Kang, B. Zhang, X. Wu, C. Y. Liu and J. Gao, Sparse generalized canonical correlation analysis for biological model integration: a genetic study of psychiatric disorders, in 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2013), 1490–1493. [18] J. R. Kettenring, Canonical analysis of several sets of variables, Biometrika, 58 (1971), 433-451.  doi: 10.1093/biomet/58.3.433. [19] Y. Luo, D. Tao, K. Ramamohanarao, C. Xu and Y. G. Wen, Tensor canonical correlation analysis for multi-view dimension reduction, IEEE Trans. Knowl. Data Eng., 27 (2015), 3111-3124.  doi: 10.1109/TKDE.2015.2445757. [20] S. Osher, M. Burger, D. Goldfarb, J. J. Xu and W. T. Ying, An iterative regularization method for total variation-based image restoration, Multiscale Model. Simul., 4 (2005), 460-489.  doi: 10.1137/040605412. [21] R. Steinberger, B. Pouliquen, A. Widiger and et al., The jrc-acquis: A multilingual aligned parallel corpus with 20+ languages, in Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC$\prime$ 2006), (2006), 2142–2147. [22] J. V$\acute{I}$a, I. Santamar$\acute{I}$a and and J. P$\acute{e}$rez, A learning algorithm for adaptive canonical correlation analysis of several data sets, Neural Netw., 20 (2007), 139-152.  doi: 10.1016/j.neunet.2006.09.011. [23] A. Vinokourov, N. Cristianini and J. Shawe-Taylor, Inferring a semantic representation of text via cross-language correlation analysis, in Advances in Neural Information Processing Systems, (2003), 1497–1504. [24] S. Waaijenborg, P. C. V. de Witt Hamer and A. H. Zwinderman, Quantifying the association between gene expressions and dna-markers by penalized canonical correlation analysis, Stat. Appl. Genet. Mol. Biol., 7 (2008), Art. 3. doi: 10.2202/1544-6115.1329. [25] D. M. Witten, R. Tibshirani and T. Hastie, A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis, Biostatistics, 10 (2009), 515-534.  doi: 10.1093/biostatistics/kxp008. [26] Y. Yamanishi, J. P. Vert, A. Nakaya and M. Kanehisa, Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis, Bioinformatics, 19 (2003), i323–i330. doi: 10.1093/bioinformatics/btg1045. [27] W. Yin, S. Osher, D. Goldfarb and J. Darbon, Bregman iterative algorithms for 1-minimization with applications to compressed sensing, SIAM J. Imaging Sci., 1 (2008), 143-168.  doi: 10.1137/070703983.
original signal
The samples of JRC-Acquis database that used in our experiments
 Algorithm 1: Sparse Generalized CCA algorithm. Input:   Training data $X_j\in \mathbb{R}^{n_j\times m}$ ($j=1,\cdots,J$), and tolerance parameter $\varepsilon$. Output:  Sparse canonical variates $W$. 1: Compute reduced SVD for each $X_j$ via Equation (3.1). 2: Compute top $\ell$ eigenvectors of matrix $M$. 3: Construct $G$ (setting the top $\ell$ eigenvectors of $M$ as the rows of $G$). 4: Let $W^0=U^0=0$. 5: while error$>\varepsilon$ do 6:   Compute $(U^{k+1}, W^{k+1})$ via (3.6), 7:  error= $\|AW-B\|_F$, 8:end while 9:return $W=(W^T_1,\cdots,W_J^T)^T$.
 Algorithm 1: Sparse Generalized CCA algorithm. Input:   Training data $X_j\in \mathbb{R}^{n_j\times m}$ ($j=1,\cdots,J$), and tolerance parameter $\varepsilon$. Output:  Sparse canonical variates $W$. 1: Compute reduced SVD for each $X_j$ via Equation (3.1). 2: Compute top $\ell$ eigenvectors of matrix $M$. 3: Construct $G$ (setting the top $\ell$ eigenvectors of $M$ as the rows of $G$). 4: Let $W^0=U^0=0$. 5: while error$>\varepsilon$ do 6:   Compute $(U^{k+1}, W^{k+1})$ via (3.6), 7:  error= $\|AW-B\|_F$, 8:end while 9:return $W=(W^T_1,\cdots,W_J^T)^T$.
Comparison results on synthetic dataset over $30$ training-testing replications: reconstruction error for training data (Trerror), reconstruction error for testing data (Tserror), and sparsity (Spar1, Spar2, Spar3 stand for the sparsity of the first view, second view, and third view, respectively) obtained by GCCA, Deep GCCA, WGCCA and SGCCA algorithms
 GCCA Deep GCCA WGCCA SGCCA Trerror 3.7277e-30 0.5043 1.1118e-07 1.6354e-10 Tserror 0.4756 0.2955 0.6860 0.4554 Spar1 (%) 2.73 0.01 70.01 99.47 Spar2 (%) 5.15 0.03 78.19 99.67 Spar3 (%) 7.54 0.09 83.02 99.72
 GCCA Deep GCCA WGCCA SGCCA Trerror 3.7277e-30 0.5043 1.1118e-07 1.6354e-10 Tserror 0.4756 0.2955 0.6860 0.4554 Spar1 (%) 2.73 0.01 70.01 99.47 Spar2 (%) 5.15 0.03 78.19 99.67 Spar3 (%) 7.54 0.09 83.02 99.72
Data Structures: data dimension ($n$), number of data ($m$), number of classes ($K$), number of columns in $W_1$ and $W_2$ ($\ell$)
 Type Data $n$ $m$ $K$ $\ell$ Gene Data Leukemia 3571 72 2 1 Lymphomia 4026 62 3 2 Prostate 6033 102 2 1 Brain 5597 42 5 4
 Type Data $n$ $m$ $K$ $\ell$ Gene Data Leukemia 3571 72 2 1 Lymphomia 4026 62 3 2 Prostate 6033 102 2 1 Brain 5597 42 5 4
Comparison results on gene data over $30$ training-testing replications: the average of sparsity (Spar1 and Spar2 stand for the sparsity of the first view and second view, respectively), classification accuracy, and the summation of correlation coefficients (SCORR)
 Methods Spar1($\%$) Spar2($\%$) Accuracy ($\%$) SCORR Leukemia CCA 0.03 0 97.20 0.8945 PMD CCA 98.48 0 72.22 0.8944 GCCA 5.80 50 97.20 0.9227 Deep CCA 0 0 94.40 0.8662 SGCCA 98.99 50 100 0.8916 Lyphomia CCA 0.04 0 83.87 1.8206 PMD CCA 85.38 0 77.97 1.7911 GCCA 5.77 50 90.32 1.7423 Deep CCA 0 0 80.65 0.9754 SGCCA 99.23 50 96.77 1.6933 Prostate CCA 0.1 0 88.24 0.7646 PMD CCA 99.19 0 60.78 0.2978 GCCA 4.59 50 88.24 0.7645 Deep CCA 0 0 79.41 0.7610 SGCCA 99.14 50 85.27 0.7758 Brain CCA 0.07 0 71.43 2.9749 PMD CCA 76.40 74.65 47.62 3.0669 GCCA 4.52 34.9 61.90 2.8307 Deep CCA 0 0 61.90 2.9674 SGCCA 99.62 34.9 66.67 2.5729
 Methods Spar1($\%$) Spar2($\%$) Accuracy ($\%$) SCORR Leukemia CCA 0.03 0 97.20 0.8945 PMD CCA 98.48 0 72.22 0.8944 GCCA 5.80 50 97.20 0.9227 Deep CCA 0 0 94.40 0.8662 SGCCA 98.99 50 100 0.8916 Lyphomia CCA 0.04 0 83.87 1.8206 PMD CCA 85.38 0 77.97 1.7911 GCCA 5.77 50 90.32 1.7423 Deep CCA 0 0 80.65 0.9754 SGCCA 99.23 50 96.77 1.6933 Prostate CCA 0.1 0 88.24 0.7646 PMD CCA 99.19 0 60.78 0.2978 GCCA 4.59 50 88.24 0.7645 Deep CCA 0 0 79.41 0.7610 SGCCA 99.14 50 85.27 0.7758 Brain CCA 0.07 0 71.43 2.9749 PMD CCA 76.40 74.65 47.62 3.0669 GCCA 4.52 34.9 61.90 2.8307 Deep CCA 0 0 61.90 2.9674 SGCCA 99.62 34.9 66.67 2.5729
Comparison results on JRC-Acquis database over $30$ training-testing replications for $\ell = 100$: reconstruction error for training data (Trerror), reconstruction error for testing data (Tserror), and sparsity (Spar1, Spar2, Spar3 stand for the sparsity of the first view, second view, and third view, respectively) obtained by GCCA, WGCCA and SGCCA algorithms
 GCCA Deep GCCA WGCCA SGCCA Trerror 2.2078e-30 3.0859e-02 8.4776e-12 5.5140e-11 Tserror 4.1496e-02 1.7648e-02 0.7138 3.8799e-02 Spar1 (%) 7.00 0.1140 10.27 96.56 Spar2 (%) 7.24 0.6850 9.96 96.85 Spar3 (%) 6.09 0.1100 8.49 95.64
 GCCA Deep GCCA WGCCA SGCCA Trerror 2.2078e-30 3.0859e-02 8.4776e-12 5.5140e-11 Tserror 4.1496e-02 1.7648e-02 0.7138 3.8799e-02 Spar1 (%) 7.00 0.1140 10.27 96.56 Spar2 (%) 7.24 0.6850 9.96 96.85 Spar3 (%) 6.09 0.1100 8.49 95.64
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