Twitter has provided people with an effective way to communicate and interact with each other. It is an undisputable fact that people's influence plays an important role in disseminating information over the Twitter social network. Although a number of research work on finding influential users have been reported in the literature, they never really seek to distinguish and analyze different inf...

A framework for multi-agent based clustering is described whereby individual agents represent individual clusters. A particular feature of the framework is that, after an initial cluster configuration has been generated, the agents are able to negotiate with a view to improving on this initial clustering. The framework can be used in the context of a number of clustering paradigms, two are inve...

Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can ...

In many unsupervised learning problems data can be available in different representations, often referred to as views. By leveraging information from multiple views we can obtain clustering that is more robust and accurate compared to the one obtained via the individual views. We propose a novel algorithm that is based on neighborhood co-regularization of the clustering hypotheses and that sear...

0167-8655/$ see front matter 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.patrec.2013.12.003 q This paper has been recommended for acceptance by Jesús Ariel Carrasco Ochoa. ⇑ Corresponding author. Tel.: +358 41 4996553. E-mail addresses: [email protected], [email protected] (X. Zhao), [email protected] (N. Evans), [email protected] (J.-L. Dugelay). Xuran Zhao ⇑, Nichol...

We propose a spectral clustering algorithm for the multi-view setting where we have access to multiple views of the data, each of which can be independently used for clustering. Our spectral clustering algorithm has a flavor of co-training, which is already a widely used idea in semi-supervised learning. We work on the assumption that the true underlying clustering would assign a point to the s...

Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

In this paper, we present a hyper-Laplacian regularized method WHLR-MSC with new weighted tensor nuclear norm for multi-view subspace clustering. Specifically, firstly stack the representation matrices of different views into tensor, which neatly captures higher-order correlations between views. Secondly, in order to make all singular values have contributions based on tensor-Singular Value Dec...

In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine...