Regularized Co-Clustering with Dual Supervision

By attempting to simultaneously partition both the rows (examples) and columns (features) of a data matrix, Co-clustering algorithms often demonstrate surprisingly impressive performance improvements over traditional one-sided row clustering techniques. A good clustering of features may be seen as a combinatorial transformation of the data matrix, effectively enforcing a form of regularization that may lead to a better clustering of examples (and vice-versa). In many applications, partial supervision in the form of a few row labels as well as column labels may be available to potentially assist co-clustering. In this paper, we develop two novel semi-supervised multi-class classification algorithms motivated respectively by spectral bipartite graph partitioning and matrix approximation formulations for co-clustering. These algorithms (i) support dual supervision in the form of labels for both examples and/or features, (ii) provide principled predictive capability on out-of-sample test ...

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic
• Keywords: Algorithms, Data Matrix, Information Technology, NIPS 2008, Row Clustering Techniques