Abstract:

Non-negative matrix factorization (NMF) and concept factorization (CF) can be found not to make use of the power of kernelization or pay any attention to the geometric structure and the label information of the data. A novel algorithm called manifold regularizedbased discriminant concept factorization (MRCF). When original data is factorized in lower dimensional space using CF, MRCF preserves the intrinsic geometry of data, using the label information as supervised learning, producing an efficient multiplicative updating procedure and providing the convergence proof of our algorithm. Compared with NMF, CF and its improved algorithms, experimental results of ORL face database, COIL20 image database and USPS handwrite database have shown that the proposed method achieves more highly clustering precision.

Key words: image clustering; manifold learning; concept factorization; discriminant analysis