Non-negative matrix factorization for visual coding
Weixiang Liu, Nanning Zheng, Xiaofeng Lu · 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). · 2003
This paper combines linear spun coding and nonnegative matrix factorization into sparse non-negative matrix factorization. In contrast to non-negative matrix factorization, the new model can learn much sparser representation via imposing sparseness constraints explicitly; in contrast to a close model -non-negative sparse coding, the new model can learn parts-based representation via fully multiplicative updates because of adapting a generalized Kullback-Leibler divergence instead of the conventional mean error for approximation error. Experiments on MIT-CBCL training facts data demonstrate the effectiveness of the proposed method.