Regularized nonnegative shared subspace learning

Data Mining and Knowledge Discovery

Volumn 26, Issue 1, 2013, Pages 57-97

  Gupta, Sunil Kumar ^a   Phung, Dinh ^a   Adams, Brett ^a   Venkatesh, Svetha ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

Auxiliary sources; Multi task clustering; Nonnegative shared subspace learning; Transfer learning

Indexed keywords

AUXILIARY SOURCE; CLUSTERING APPLICATIONS; DATA MINING TASKS; DATA-SOURCES; FORMAL FRAMEWORK; JOINT ANALYSIS; JOINT MODELING; MULTI-TASK CLUSTERING; NONNEGATIVE MATRIX FACTORIZATION; ORTHOGONALITY CONSTRAINTS; PERFORMANCE DEGRADATION; REAL WORLD DATA; SUBSPACE LEARNING; TRANSFER LEARNING;

COMPUTER APPLICATIONS;

DATA MINING;

EID: 84872397219     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-011-0244-8     Document Type: Article

References (58)

1. Agarwal A, Daumé H III, Gerber S (2010) Learning multiple tasks using manifold regularization. In: Advances in neural information processing systems, vol 23, pp 46-54
2. Ando R, Zhang T (2005) A framework for learning predictive structures from multiple tasks and unlabeled data. J Mach Learn Res 6: 1817-1853
3. Bae E, Bailey J (2006) Coala: a novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In: IEEE international conference on data mining, pp 53-62
4. Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley, Reading, MA
5. Baxter J (2000) A model of inductive bias learning. J Artif Intell Res 12: 149-198
6. Ben-David S, Schuller R (2003) Exploiting task relatedness for multiple task learning. In: 16th annual conference on computational learning theory, vol 2777, pp 567-580
7. Berry M, Browne M (2005) Email surveillance using non-negative matrix factorization. Comput Math Organ Theory 11(3): 249-264
8. Berry M, Browne M, Langville A, Pauca V, Plemmons R (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 52(1): 155-173
9. Bickel S, Scheffer T (2004) Multi-view clustering. In: Proceedings of the IEEE international conference on data mining, pp 19-26
10. Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3: 993-1022
11. Boutsidis C, Gallopoulos E (2008) SVD based initialization: a head start for nonnegative matrix factorization. Pattern Recogn 41(4): 1350-1362
12. Bucak S, Gunsel B (2007) Video content representation by incremental non-negative matrix factorization. ICIP 2: 113-116
13. Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. In: International conference on computer vision, pp 1-7
14. Cai D, He X, Wu X, Han J (2008) Non-negative matrix factorization on manifold. IEEE international conference on data mining, pp 63-72
15. Cai D, He X, Han J, Huang T (2011) Graph regularized non-negative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8): 1548-1560
16. Caruana R (1997) Multitask learning. Mach Learn 28(1): 41-75
17. Chaudhuri K, Kakade S, Livescu K, Sridharan K (2009) Multi-view clustering via canonical correlation analysis. In: Proceedings of the 26th international conference on machine learning, pp 129-136
18. Choi S (2008) Algorithms for orthogonal nonnegative matrix factorization. In: Proceedings of the international joint conference on neural networks, pp 1828-1832
19. Cui Y, Fern X, Dy J (2007) Non-redundant multi-view clustering via orthogonalization. In: IEEE international conference on data mining. IEEE, pp 133-142
20. Dai W, Jin O, Xue G, Yang Q, Yu Y (2009) Eigentransfer: a unified framework for transfer learning. ICML, pp 193-200
21. Dempster A, Laird N, Rubin D et al (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol) 39(1): 1-38
22. Ding C, Li T, Peng W, Park H (2006) Orthogonal nonnegative matrix tri-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 126-135
23. Duda R, Hart P, Stork D (2001) Pattern classification, vol 2. Wiley-Interscience, New York
24. Gu Q, Zhou J (2009a) Learning the shared subspace for multi-task clustering and transductive transfer classification. In: IEEE international conference on data mining, pp 159-168
25. Gu Q, Zhou J (2009b) Local learning regularized nonnegative matrix factorization. In: Proceedings of the 21st international joint conference on artificial intelligence, pp 1046-1051
26. Gupta S, Phung D, Adams B, Tran T, Venkatesh S (2010) Nonnegative shared subspace learning and its application to social media retrieval. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1169-1178
27. Gupta S, Phung D, Adams B, Venkatesh S (2011a) A Bayesian framework for learning shared and individual subspaces from multiple data sources. In: Advances in knowledge discovery and data mining, 15th Pacific-Asia conference (PAKDD), pp 136-147
28. Gupta SK, Phung D, Adams B, Venkatesh S (2011b) A matrix factorization framework for jointly analyzing multiple nonnegative data sources. In: Proceedings of text mining workshop, in conjuction with SIAM international conference on data mining
29. Hardoon D, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12): 2639-2664
30. Ji S, Tang L, Yu S, Ye J (2008) Extracting shared subspace for multi-label classification. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 381-389
31. Jolliffe I (2002) Principle component analysis. Springer, Heidelberg
32. Kailing K, Kriegel H, Pryakhin A, Schubert M (2004) Clustering multi-represented objects with noise. In: Advances in knowledge discovery and data mining, 8th Pacific-Asia conference (PAKDD), pp 394-403
33. Kim H, Park H (2008) Non-negative matrix factorization based on alternating non-negativity constrained least squares and active set method. SIAM J Matrix Anal Appl 30(2): 713-730
34. Langville A, Meyer C, Albright R, Cox J, Duling D (2006) Initializations for the nonnegative matrix factorization. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining
35. Lee D, Seung H (2001) Algorithms for non-negative matrix factorization. Adv Neural Inform Process Syst 13: 556-562
36. Li T, Ma S, Ogihara M (2004) Document clustering via adaptive subspace iteration. In: Proceedings of the 27th international ACM SIGIR conference on research and development in information retrieval, pp 218-225
37. Lin C (2007) Projected gradient methods for nonnegative matrix factorization. Neural Comput 19(10): 2756-2779
38. Lin Y, Sundaram H, De Choudhury M, Kelliher A (2009) Temporal patterns in social media streams: theme discovery and evolution using joint analysis of content and context. In: IEEE international conference on multimedia and expo, pp 1456-1459
39. Lovász L, Plummer M (1986) Matching theory. Elsevier, Amsterdam
40. Manning C, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge
41. Mardia KV, Bibby JM, Kent JT (1979) Multivariate analysis. Academic, New York
42. Niu D, Dy J, Jordan M (2010) Multiple non-redundant spectral clustering views. In: Proceedings of the 27th international conference on machine learning, pp 831-838
43. Pan S, Yang Q (2008) A survey on transfer learning. Technical Report HKUST-CS08-08. Department of Computer Science and Engineering, HKUST, Hong Kong
44. Qi Z, Davidson I (2009) A principled and flexible framework for finding alternative clusterings. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 717-726
45. Rui Y, Huang T (2000) Optimizing learning in image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Published by the IEEE Computer Society, pp 236-243
46. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inform Process Manag 24(5): 513-523
47. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8): 888-905
48. Si S, Tao D, Geng B (2009) Bregman divergence based regularization for transfer subspace learning. IEEE Trans Knowl Data Eng 22(7): 929-942
49. Thrun S (1996) Is learning the n-th thing any easier than learning the first? In: Advances in neural information processing systems, pp 640-646
50. Wild S, Curry J, Dougherty A (2004) Improving non-negative matrix factorizations through structured initialization. Pattern Recogn 37(11): 2217-2232
51. Wiswedel B, Höppner F, Berthold M (2010) Learning in parallel universes. Data Min Knowl Disc 21(1): 130-152
52. Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th international ACM SIGIR conference on research and development in information retrieval. ACM, New York, NY, pp 267-273
53. Yan R, Tesic J, Smith J (2007) Model-shared subspace boosting for multi-label classification. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 834-843
54. Yang T, Jin R, Jain A, Zhou Y, Tong W (2010) Unsupervised transfer classification: application to text categorization. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1159-1168
55. Yu K, Zhu S, Lafferty J, Gong Y (2009) Fast nonparametric matrix factorization for large-scale collaborative filtering. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, pp 211-218
56. Zhang J, Zhang C (2011) Multitask Bregman clustering. Neurocomputing 74(10): 1720-1734
57. Zhou D, Bousquet O, Lal T, Weston J, Schölkopf B (2004) Learning with local and global consistency. Adv Neural Inform Process Syst 16: 595-602
58. Zhuang F, Luo P, Shen Z, He Q, Xiong Y, Shi Z, Xiong H (2010) Collaborative dual-plsa: mining distinction and commonality across multiple domains for text classification. CIKM, pp 359-368