Multi-label linear discriminant analysis

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

Volumn 6316 LNCS, Issue PART 6, 2010, Pages 126-139

  Wang, Hua ^a   Ding, Chris ^a   Huang, Heng ^a  

^a The University of Texas at Arlington   (United States)

Author keywords

Image annotation; Multi label classification; Multi label linear discriminant analysis

Indexed keywords

COMPUTER VISION; DISCRIMINANT ANALYSIS; IMAGE ANNOTATION;

CLASSIFICATION ACCURACY; DIMENSIONALITY REDUCTION; EXPERIMENTAL EVALUATION; LINEAR DISCRIMINANT ANALYSIS; MULTI LABEL CLASSIFICATION; MULTI-CLASS CLASSIFICATION; MULTI-LABEL PROBLEMS; MUSIC CLASSIFICATION;

TEXT PROCESSING;

EID: 78149293310     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15567-3_10     Document Type: Conference Paper

References (17)

1. Chen, G., Song, Y., Wang, F., Zhang, C.: Semi-supervised Multi-label Learning by Solving a Sylvester Equation. In: Proc. of SDM (2008)
2. Fukunaga, K.: Introduction to statistical pattern recognition (1990)
3. Ji, S., Tang, L., Yu, S., Ye, J.: Extracting shared subspace for multi-label classification. In: Proc. of SIGKDD, pp. 381-389 (2008)
4. Ji, S., Ye, J.: Linear Dimensionality Reduction for Multi-label Classification. In: Proc. of IJCAI, pp. 1077-1082 (2009)
5. Kang, F., Jin, R., Sukthankar, R.: Correlated label propagation with application to multi-label learning. In: Proc. of CVPR, pp. 1719-1726 (2006)
6. Lewis, D., Yang, Y., Rose, T., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361-397 (2004)
7. Liu, Y., Jin, R., Yang, L.: Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: Proc. of AAAI, p. 421 (2006)
8. Park, C., Lee, M.: On applying linear discriminant analysis for multi-labeled problems. Pattern Recognition Letters 29(7), 878-887 (2008)
9. Pock, T., Schoenemann, T., Graber, G., Bischof, H., Cremers, D.: A convex formulation of continuous multi-label problems. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 792-805. Springer, Heidelberg (2008)
10. Qi, G., Hua, X., Rui, Y., Tang, J., Mei, T., Zhang, H.: Correlative multi-label video annotation. In: Proc. of ACM Multimedia, pp. 17-26 (2007)
11. Smeaton, A., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: Proc. of MIR, p. 330 (2006)
12. Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proc. of ACM Multimedia, pp. 421-430 (2006)
13. Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multilabel classification of music into emotions. In: Proc. of ISMIR (2008)
14. Ueda, N., Saito, K.: Single-shot detection of multiple categories of text using parametric mixture models. In: Proc. of SIGKDD, pp. 626-631 (2002)
15. Wang, H., Huang, H., Ding, C.: Image Annotation Using Multi-label Correlated Greens Function. In: Proc. of ICCV (2009)
16. Yu, K., Yu, S., Tresp, V.: Multi-label informed latent semantic indexing. In: Proc. of SIGIR, p. 265 (2005)
17. Zhang, Y., Zhou, Z.: Multi-Label Dimensionality Reduction via Dependence Maximization. In: Proc. of AAAI, pp. 1503-1505 (2008)