TY - GEN
T1 - Bayesian supervised multilabel learning with coupled embedding and classification
AU - Gönen, Mehmet
PY - 2012
Y1 - 2012
N2 - Coupled training of dimensionality reduction and classifica- Tion is proposed previously to improve the prediction per- formance for single-label problems. Following this line of research, in this paper, we introduce a novel Bayesian su- pervised multilabel learning method that combines linear di- mensionality reduction with linear binary classification. We present a deterministic variational approximation approach to learn the proposed probabilistic model for multilabel clas- sification. We perform experiments on four benchmark mul- Tilabel learning data sets by comparing our method with four baseline linear dimensionality reduction algorithms. Exper- iments show that the proposed approach achieves good per- formance values in terms of hamming loss, macro F1, and micro F1 on held-out test data. The low-dimensional em- beddings obtained by our method are also very useful for exploratory data analysis.
AB - Coupled training of dimensionality reduction and classifica- Tion is proposed previously to improve the prediction per- formance for single-label problems. Following this line of research, in this paper, we introduce a novel Bayesian su- pervised multilabel learning method that combines linear di- mensionality reduction with linear binary classification. We present a deterministic variational approximation approach to learn the proposed probabilistic model for multilabel clas- sification. We perform experiments on four benchmark mul- Tilabel learning data sets by comparing our method with four baseline linear dimensionality reduction algorithms. Exper- iments show that the proposed approach achieves good per- formance values in terms of hamming loss, macro F1, and micro F1 on held-out test data. The low-dimensional em- beddings obtained by our method are also very useful for exploratory data analysis.
UR - http://www.scopus.com/inward/record.url?scp=84880216558&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880216558&partnerID=8YFLogxK
U2 - 10.1137/1.9781611972825.32
DO - 10.1137/1.9781611972825.32
M3 - Conference contribution
AN - SCOPUS:84880216558
SN - 9781611972320
T3 - Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
SP - 367
EP - 378
BT - Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PB - Society for Industrial and Applied Mathematics Publications
T2 - 12th SIAM International Conference on Data Mining, SDM 2012
Y2 - 26 April 2012 through 28 April 2012
ER -