Multilinear decomposition and topographic mapping of binary tensors

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6352 LNCS, Issue PART 1, 2010, Pages 317-326

  Mažgut, Jakub ^a   Tiňo, Peter ^b   Bodén, Mikael ^c   Yan, Hong ^d  

^a SLOVAK UNIVERSITY OF TECHNOLOGY   (Slovakia)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^c UNIVERSITY OF QUEENSLAND   (Australia)

^d CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DOCUMENT ANALYSIS; GAIT RECOGNITION; GRAPH MINING; HIGHER-ORDER STRUCTURE; LINEAR MODEL; PCA MODEL; REAL-WORLD PROBLEM; RECONSTRUCTION ERROR; TENSOR DECOMPOSITION; TOPOGRAPHIC MAPPING;

BINARY SEQUENCES; DNA SEQUENCES; EXPERIMENTS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

TENSORS;

EID: 78049400954     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15819-3_42     Document Type: Conference Paper

References (11)

1. Lu, H., Plataniotis, K. N., Venetsanopoulos, A. N.: MPCA: Multilinear Principal Component Analysis of Tensor Objects. IEEE Trans. on Neural Networks 19, 18-39 (2008)
2. Cai, D., He, X., Han, J.: Tensor Space Model for Document Analysis. In: Proc. 29th Annu. ACM SIGIR Int. Conf. Research and Development in Information Retrieval, Seatlle, WA, pp. 625-626 (August 2006)
3. De Lathauwer, L., De Moor, B., Vandewalle, J.: A Multilinear Singular Value Decomposition. SIAM Journal on Matrix Analysis and Applications 21(4), 1253-1278 (2000)
4. Wang, H., Ahuja, N.: Compact Representation of Multidimensional Data Using Tensor Rank-One Decomposition. In: Proc. 17th Int. Conf. Pattern Recognition, Cambridge, UK, pp. 44-47 (August 2004)
5. Mažgut, J., Tiňo, P., Bodén, M., Yan, H.: Generalized Multi-Linear Principal Component Analysis of Binary Tensors. Technical Report CSRP-07-10, University of Birmingham, School of Computer Science, UK (2010), http://www.cs.bham.ac.uk/~pxt/PAPERS/bin-tensor-tr.pdf
6. Schein, A., Saul, L., Ungar, L.: A Generalized Linear Model for Principal Component Analysis of Binary Data. In: 9th Int. Workshop Artificial Intelligence and Statistics, Key West, FL (January 2003)
7. Cortes, C., Mohri, M.: AUC Optimization vs. Error Rate Minimization. In: Advances in Neural Information Processing Systems, Banff, AL, Canada, vol. 16, pp. 313-320 (July 2003)
8. Li, X., Zeng, J., Yan, H.: PCA-HPR: A Principle Component Analysis Model for Human Promoter Recognition. Bioinformation 2(9), 373-378 (2008)
9. Cross, S., Clark, V., Bird, A.: Isolation of CpG islands from large genomic clones. Nucleic Acids Res. 27(10), 2099-2107 (1999)
10. Bodén, M., Bailey, T. L.: Associating transcription factor-binding site motifs with target GO terms and target genes. Nucleic Acids Res. 36(12), 4108-4117 (2008)
11. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., Sherlock, G.: Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 25(1), 25-29 (2000)