An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space
Abstract
In this paper, we propose a general formulation for kernel nonnegative matrix factorization with flexible kernels. Specifically, we propose the Gaussian nonnegative matrix factorization (GNMF) algorithm by using the Gaussian kernel in the framework. Different from a recently developed polynomial NMF (PNMF), GNMF finds basis vectors in the kernel-induced feature space and the computational cost is independent of input dimensions. Furthermore, we prove the convergence and nonnegativity of decomposition of our method. Extensive experiments compared with PNMF and other NMF algorithms on several face databases, validate the effectiveness of the proposed method. Daoqiang Zhang, Wanquan Liu
Cite
Text
Zhang and Liu. "An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space." International Joint Conference on Artificial Intelligence, 2009.Markdown
[Zhang and Liu. "An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space." International Joint Conference on Artificial Intelligence, 2009.](https://mlanthology.org/ijcai/2009/zhang2009ijcai-efficient/)BibTeX
@inproceedings{zhang2009ijcai-efficient,
title = {{An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space}},
author = {Zhang, Daoqiang and Liu, Wanquan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2009},
pages = {1345-1350},
url = {https://mlanthology.org/ijcai/2009/zhang2009ijcai-efficient/}
}