Co-Adjustment Learning for Co-Clustering

Abstract

Co-clustering simultaneously performs clustering on the sample and feature dimensions of the data matrix, so it can obtain better insight into the data than traditional clustering. Adjustment learning extracts valuable information from chunklets for unsupervised cluster learning in specific scenarios, but in fact it can be easily extended to semi-supervised and supervised learning situations. In this paper, we propose a novel co-clustering framework, named co-adjustment learning for co-clustering (CALCC), and CALCC can be simultaneously used in unsupervised, semi-supervised and supervised learning situations. A novel co-adjustment learning (CAL) model is proposed to extract meaningful representations in both sample space and feature space for co-clustering. CAL can not only perform the sample projection as well as feature projection under the guidance of chunklet information, it can also transform the original data into another space with improved separability. We can obtain the row partition matrix and column partition matrix by performing the clustering process on the representations learned by the CAL model. In order to prove the availability of our framework, an unsupervised case of CALCC is introduced to make an extensive comparison with several related methods (specifically including the classic co-clustering methods and the state-of-the-art methods closely related to our work) on several image and real data sets. The experimental results show the superior performance of the CAL model in discovering discriminative representations and demonstrate the effectiveness of the CALCC framework. The proposed CALCC framework, as demonstrated in the experiments, is more effective superior to the related methods. In addition, the chunklet information can be effective to enhance the expression ability of the learned representations.