Human perception is capable of integrating local events to generate an overall impression at the global level; this is evident in daily life and is utilized repeatedly in behavioral science studies to bring objective measures into studies of human behavior. In this work, we explore two hypotheses considering whether it is the isolated-saliency or the causal-integration of information that can t...

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Co-clustering exploits co-occurrence information, from contingency tables to cluster both rows and columns simultaneously. It has been established that co-clustering produces a better clustering structure as compared to conventional methods of clustering. So far, co-clustering has only been used as a technique for producing hard clusters, which might be inadequate for applications such as docum...

In this paper, we propose an online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, positive negative instance pairs are constructed through data augmentations then projected into feature space. Therein, learning respectively conducted in row column space by maximizing simil...

Clustering under constraints is a recent innovation in the artificial intelligence community that has yielded significant practical benefit. However, recent work has shown that for some negative forms of constraints the associated subproblem of just finding a feasible clustering is NP-complete. These worst case results for the entire problem class say nothing of where and how prevalent easy pro...

A multiclass classification method based on output design p. 15 Regularized semi-supervised classification on manifold p. 20 Similarity-based sparse feature extraction using local manifold learning p. 30 Generalized conditional entropy and a metric splitting criterion for decision trees p. 35 RNBL-MN : a recursive naive Bayes learner for sequence classification p. 45 TRIPPER : rule learning usi...

We explore the use of instance and cluster-level constraints with agglomerative hierarchical clustering. Though previous work has illustrated the benefits of using constraints for non-hierarchical clustering, their application to hierarchical clustering is not straight-forward for two primary reasons. First, some constraint combinations make the feasibility problem (Does there exist a single fe...