Multilayer concept factorization for data representation

Previous studies have demonstrated that Concept Factorization (CF) have yielded impressive results for dimensionality reduction and data representation. However, it is difficult to get a desired result by using single layer concept factorization for some complex data, especially for ill-conditioned and badly scaled data. To improve the performance of the existing CF algorithms, in this paper, we proposed a novel clustering approach, called Multilayer Concept Factorization (MCF), for data representation. MCF is a cascade connection of L mixing subsystems to decompose the observation matrix iteratively in a number of layers. Meanwhile, we explore the corresponding update solutions of the MCF method to reduce the risk of getting stuck in local minima in non-convex alternating computations. Experimental results on document and face dataset demonstrate that our proposed method achieves better clustering performance in terms of accuracy and normalized mutual information in clustering.