Non-Negative Feature Extraction using Conjugate Gradient Method

Non-negative matrix factorization (NMF) is an efficient approach for non-negative feature extraction and parts-based representation. Nevertheless, the optimization problem of NMF is usually resolved using gradient descent (GD) method, which leads to slow convergence. In this paper, we utilize conjugate gradient method (CGM) to develop a novel fast CGM-based NMF (CGM-NMF) algorithm for face recogition. The update rules of our CGM-NMF are acquired by searching conjugate directions under non-negativity constraint. The proposed CGM-NMF algorithm is theoretically proven to be convergent. Experimental results show that our CGM-NMF method has superior performance.