0467543 20240103213205.420161219235959.9 85019092045 000406771303097 10.1109/ICPR.2016.7900193 6 s. P cav_un_epca*0467540978-1-5090-4846-53592-3597PiscatawayIEEE2016 Visualization matrix factorization cav_un_auth*0021088 25 Kudo M. JP cav_un_auth*0037626 25 Kimura K. JP cav_un_auth*0101093 Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition 25 Haindl Michal UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0339754 25 Tenmoto H. JP http://library.utia.cas.cz/separaty/2016/RO/haindl-0467543.pdf cav_un_auth*0339755 15H02719 JSPS KAKENHI JP Visualization helps us to understand single-label and multi-label classification problems. In this paper, we show several standard techniques for simultaneous visualization of samples, features and multi-classes on the basis of linear regression and matrix factorization. The experiment with two real-life multilabel datasets showed that such techniques are effective to know how labels are correlated to each other and how features are related to labels in a given multi-label classification problem. cav_un_auth*0340495 23rd International Conference on Pattern Recognition ICPR 2016 20161204 20161208 Cancún MX BD 2017 4 PO RVO:67985556 http://hdl.handle.net/11104/0266461 S 88 3+4 Proceedings Paper Computer Science Artificial Intelligence 2016 85019092045 SCOPUS PUBMED 000406771303097 WOS cav_un_epca*0467540 Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 978-1-5090-4846-5 3592 3597 Piscataway IEEE 2016