Subspace Clustering with Active Learning

Published in 2019 IEEE International Conference on Big Data, 2019

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 segmentation and face clustering. It assumes that data originate from a union of subspaces, and clusters the data depending on their corresponding subspaces. In practice, it is reasonable to assume that a limited amount of labels can be obtained, potentially at a cost. Therefore, algorithms that can effectively and efficiently incorporate this information to improve the clustering model are desirable. Read more

Recommended citation: H. Peng, and N. Pavlidis, "Subspace Clustering with Active Learning", Proceedings of 2019 IEEE International Conference on Big Data, 2019.

Subspace Clustering of Very Sparse High-Dimensional Data

Published in 2018 IEEE International Conference on Big Data, 2018

In this paper we study the problem of clustering collections of very short texts using subspace clustering. This problem arises in many application areas such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both high-dimensional, due to the large number of unique terms in the corpus, and extremely sparse, as each text contains a very small number of words with no repetition. Read more

Recommended citation: H. Peng, N. Pavlidis, I. Eckley and I. Tsalamanis, "Subspace Clustering of Very Sparse High-Dimensional Data", Proceedings of 2018 IEEE International Conference on Big Data , 2018.