Hidden Markov Random Fields Based LSI Text Semi-supervised Clustering

Clustering Heterogeneous Data with Mutual Semi-supervision

We propose a new methodology for clustering data comprising multiple domains or parts, in such a way that the separate domains mutually supervise each other within a semi-supervised learning framework. Unlike existing uses of semi-supervised learning, our methodology does not assume the presence of labels from part of the data, but rather, each of the different domains of the data separately un...

A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields

Recently, a number of methods have been proposed for semi-supervised clustering that employ supervision in the form of pairwise constraints. We describe a probabilistic model for semisupervised clustering based on Hidden Markov Random Fields (HMRFs) that incorporates relational supervision. The model leads to an EMstyle clustering algorithm, the E-step of which requires collective assignment of...

Probabilistic Semi-Supervised Clustering with Constraints

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn th...

Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models

Semi-supervised Clustering using Combinatorial MRFs

A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised learning by showing their relationship with...