SCOAL: A Framework for Simultaneous Co-Clustering and Learning from Complex Data
For difficult classification or regression problems, practitioners often segment the data into relatively homogeneous groups and then build a predictive model for each group. This two-step procedure usually results in simpler, more interpretable and actionable models without any loss in accuracy. In this work, we consider problems such as predicting customer behavior across products, where the independent variables can be naturally partitioned into two sets, that is, the data is dyadic in nature. A pivoting operation now results in the dependent variable showing up as entries in a "customer by product" data matrix. We present the Simultaneous CO-clustering And Learning (SCOAL) framework, based on the key idea of interleaving co-clustering and construction of prediction models to iteratively improve both cluster assignment and fit of the models. This algorithm provably converges to a local minimum of a suitable cost function. The framework not only generalizes co-clustering and collaborative filtering to model-based co-clustering, but can also be viewed as simultaneous co-segmentation and classification or regression, which is typically better than independently clustering the data first and then building models. Moreover, it applies to a wide range of bi-modal or multimodal data, and can be easily specialized to address classification and regression problems. We demonstrate the effectiveness of our approach on both these problems through experimentation on a variety of datasets.
co-clustering classification regression predictive modeling multimodal data dyadic data