An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1345-1349.
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
@inproceedings{ijcai2009p226,
title = {An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space},
author = {Zhang, Daoqiang and Liu, Wanquan},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1345--1349},
year = {2009},
url = {https://ijcai.org/proceedings/2009/226},
}
TY - CPAPER TI - An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space AU - Zhang, Daoqiang AU - Liu, Wanquan T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1345 EP - 1349 UR - https://ijcai.org/proceedings/2009/226 ER -Copied
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