Tensors are increasingly common in several areas such as data mining, computer graphics, and computer vision. Tensor clustering is a fundamental tool for data analysis and pattern discovery. However, there usually exist outlying data points in realworld datasets, which will reduce the performance of clustering. This motivates us to develop a tensor clustering algorithm that is robust to the out...

Unions of subspaces provide a powerful generalization of single subspace models for collections of high-dimensional data; however, learning multiple subspaces from data is challenging due to the fact that segmentation—the identification of points that live in the same subspace—and subspace estimation must be performed simultaneously. Recently, sparse recovery methods were shown to provide a pro...

The clustering problem as a problem of set function optimization with constraints is considered. The behavior of quasi-concave functions on antimatroids and on convex geometries is investigated. The duality of these two set function optimizations is proved. The greedy type Chain algorithm, which allows to find an optimal cluster, both as the “most distant” group on antimatroids and as a dense c...

Motivated by colour constancy work in physics-based vision, we develop a new low-dimensional video frame feature that is effectively insensitive to lighting change and apply the feature to keyframe production using hierarchical clustering. The new image feature results from normalising colour channels for frames and then treating 2D histograms of chromaticity as images and compressing these. Be...

The development of next-generation sequencing technology has made it possible to generate millions of sequences from environmental samples. However, the difficulty associated with taxonomy-independent analysis increases as the sequence size expands. Most of the existing algorithms, which aim to generate operational taxonomic units (OTUs), require quadratic space and time complexity that makes t...

Clustering agents by their behaviour can be crucial for building effective agent models. Traditional clustering typically aims to group entities together based on a distance metric, where a desirable clustering is one where the entities in a cluster are spatially close together. Instead, one may desire to cluster based on actionability, or the capacity for the clusters to suggest how an agent s...