Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/103518
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Efficient orthogonal non-negative matrix factorization over stiefel manifold |
Author: | Zhang, W. Tan, M. Sheng, Q. Yao, L. Shi, Q. |
Citation: | Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM '16), 2016, vol.24-28-October-2016, pp.1743-1752 |
Publisher: | Association for Computing Machinery (ACM) |
Issue Date: | 2016 |
ISBN: | 9781450340731 |
Conference Name: | ACM International Conference on Information and Knowledge Management (CIKM '16) (24 Oct 2016 - 28 Oct 2016 : Indianapolis, IN, USA) |
Statement of Responsibility: | Wei Emma Zhang, Mingkui Tan, Quan Z. Sheng, Lina Yao, Qingfeng Shi |
Abstract: | Orthogonal Non-negative Matrix Factorization (ONMF) ap- proximates a data matrix X by the product of two lower- dimensional factor matrices: X ≈ UVT, with one of them orthogonal. ONMF has been widely applied for clustering, but it often suffers from high computational cost due to the orthogonality constraint. In this paper, we propose a method, called Nonlinear Riemannian Conjugate Gradient ONMF (NRCG-ONMF), which updates U and V alterna- tively and preserves the orthogonality of U while achiev- ing fast convergence speed. Specifically, in order to update U, we develop a Nonlinear Riemannian Conjugate Gradi- ent (NRCG) method on the Stiefel manifold using Barzilai- Borwein (BB) step size. For updating V, we use a closed- form solution under non-negativity constraint. Extensive experiments on both synthetic and real-world data sets show consistent superiority of our method over other approaches in terms of orthogonality preservation, convergence speed and clustering performance. |
Keywords: | Orthogonal NMF; Stiefel Manifold; Clustering |
Rights: | © 2016 ACM |
DOI: | 10.1145/2983323.2983761 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102270 http://purl.org/au-research/grants/arc/DP160100703 http://purl.org/au-research/grants/arc/DP140100104 http://purl.org/au-research/grants/arc/FT140101247 |
Published version: | http://dx.doi.org/10.1145/2983323.2983761 |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.