Orderly subspace clustering
Authors: Wang, J., Suzuki, A., Xu, L., Tian, F., Yang, L. and Yamanishi, K.
Journal: 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Pages: 5264-5272
ISBN: 9781577358091
Abstract:Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable for practical learning. To meet these two objectives, in this paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture the intrinsic subspace structure and reveal orderly structure that is faithful to true data relationship. Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer.
https://eprints.bournemouth.ac.uk/31937/
Source: Scopus
Orderly Subspace Clustering
Authors: Wang, J., Suzuki, A., Xu, L., Tian, F., Yang, L. and Yamanishi, K.
Journal: THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Pages: 5264-5272
eISSN: 2374-3468
ISSN: 2159-5399
https://eprints.bournemouth.ac.uk/31937/
Source: Web of Science (Lite)
Orderly Subspace Clustering
Authors: Wang, J., Suzuki, A., Linchuan, X., Tian, F., Liang, Y. and Yamanishi, K.
Conference: AAAI Conference on Artificial Intelligence
Dates: 27 January-1 February 2019
https://eprints.bournemouth.ac.uk/31937/
Source: Manual
Orderly Subspace Clustering
Authors: Wang, J., Suzuki, A., Linchuan, X., Tian, F., Liang, Y. and Yamanishi, K.
Conference: AAAI Conference on Artificial Intelligence
Abstract:Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable for practical learning. To meet these two objectives, in this paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture the intrinsic subspace structure and reveal orderly structure that is faithful to true data relationship. Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer.
https://eprints.bournemouth.ac.uk/31937/
Source: BURO EPrints