Title :
Incremental locality preserving nonnegative matrix factorization
Author :
Jianwei Zheng ; Yu Chen ; Yiting Jin ; Wanliang Wang
Author_Institution :
Coll. of Comput. Sci., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
Recently nonnegative matrix factorization (NMF) has become a popular dimension reduction method and it has been successfully applied to image processing and pattern recognition. In this paper, we propose an incremental locality preserving nonnegative matrix factorization (ILPNMF) method, which is aimed to discover the manifold structure embedded in high-dimensional space that deals well with large scale data. By assuming that the newly added samples do not change the encoding vectors of old samples, we present a cost function for online learning. Then we use projected gradient method to solve the update rule of the cost function. Experimental results show that ILPNMF provides a better parts-based representation compared with INMF and it is faster than the batch one LPNMF.
Keywords :
gradient methods; matrix decomposition; pattern classification; ILPNMF method; dimension reduction method; encoding vectors; gradient method; high-dimensional space; incremental locality preserving nonnegative matrix factorization; large scale data; manifold structure discovery; online learning; Databases; Pattern recognition; Proteins; Testing;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2013 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-6341-9
DOI :
10.1109/ICACI.2013.6748489
