TY - GEN
T1 - Semisupervised Nonnegative Matrix Factorization for learning the semantics
AU - Shen, Bin
AU - Datbayev, Zhanibek
AU - Makhambetov, Olzhas
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In real world there are a lot of unlabeled data, and relatively few labeled data. Unlabeled data help to learn a statistical model that can fully describe the global property of data, while labeled data help to minimize the gap between the statistical property and human beings' perception, i.e. labeled data can help to learn the semantics. Nonnegative Matrix Factorization is a popular technique in data analysis, since a lot of real world data are nonnegative. However, traditional NMF is an unsupervised learning algorithm, which means that it cannot make use of the label information. To enable NMF to make use of both labeled and unlabeled data samples, we propose a novel semisupervised Nonnegative Matrix Factorization technique for learning the semantics. The proposed algorithm extracts prior information from the labeled data, and then uses it to guide the later processing. Experimental results with different settings prove the efficacy of the proposed algorithm.
AB - In real world there are a lot of unlabeled data, and relatively few labeled data. Unlabeled data help to learn a statistical model that can fully describe the global property of data, while labeled data help to minimize the gap between the statistical property and human beings' perception, i.e. labeled data can help to learn the semantics. Nonnegative Matrix Factorization is a popular technique in data analysis, since a lot of real world data are nonnegative. However, traditional NMF is an unsupervised learning algorithm, which means that it cannot make use of the label information. To enable NMF to make use of both labeled and unlabeled data samples, we propose a novel semisupervised Nonnegative Matrix Factorization technique for learning the semantics. The proposed algorithm extracts prior information from the labeled data, and then uses it to guide the later processing. Experimental results with different settings prove the efficacy of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84877808578&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877808578&partnerID=8YFLogxK
U2 - 10.1109/SCIS-ISIS.2012.6505160
DO - 10.1109/SCIS-ISIS.2012.6505160
M3 - Conference contribution
AN - SCOPUS:84877808578
SN - 9781467327428
T3 - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
SP - 821
EP - 824
BT - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
T2 - 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
Y2 - 20 November 2012 through 24 November 2012
ER -