5 citations · 5 across the 5 of their papers we have counts for
5 papers
Subspace Change-Point Detection via Low-Rank Matrix Factorisation
Euan Thomas McGonigle, Hankui Peng
Multivariate time series can often have a large number of dimensions, whether it is due to the vast amount of collected features or due to how the data sources are processed. Frequ…
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning
Hankui Peng, Nicos G. Pavlidis
Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work…
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Hankui Peng, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
Superpixels serve as a powerful preprocessing tool in numerous computer vision tasks. By using superpixel representation, the number of image primitives can be largely reduced by o…
Subspace Clustering with Active Learning
Hankui Peng, Nicos G. Pavlidis
Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion…
Subspace Clustering of Very Sparse High-Dimensional Data
Hankui Peng, Nicos Pavlidis, Idris Eckley +1
In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisati…