SemiNMF-PCA framework for Sparse Data Co-clustering
Kais Allab, Lazhar Labiod, Mohamed Nadif · 2016
Several studies have demonstrated the importance of co-clustering which aims to cluster simultaneously the sets of objects and features. The co-clustering is often more effective than one-side clustering, especially when considering sparse high dimensional data. In this paper, we propose a novel way to consider the co-clustering and the reduction of the dimension simultaneously. Our approach takes advantage of the mutual reinforcement between Principal Component Analysis (PCA) which provides a low-dimensional representation of data and Semi-Nonnegative Matrix Factorization (SemiNMF) that learns this low-dimensional representation and lends itself to a co-clustering interpretation. In other words, the proposed framework aims to find an optimal subspace of multi-dimensional variables for effectively identifying a partition of the set of objects. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for co-clustering, outperforming not only spectral methods, but also co-clustering graph-regularized-based methods.