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dc.contributor.authorZhang, W.en
dc.contributor.authorTan, M.en
dc.contributor.authorSheng, Q.en
dc.contributor.authorYao, L.en
dc.contributor.authorShi, Q.en
dc.identifier.citationProceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM '16), 2016 / vol.24-28-October-2016, pp.1743-1752en
dc.description.abstractOrthogonal 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.en
dc.description.statementofresponsibilityWei Emma Zhang, Mingkui Tan, Quan Z. Sheng, Lina Yao, Qingfeng Shien
dc.publisherAssociation for Computing Machinery (ACM)en
dc.rights© 2016 ACMen
dc.subjectOrthogonal NMF; Stiefel Manifold; Clusteringen
dc.titleEfficient orthogonal non-negative matrix factorization over stiefel manifolden
dc.typeConference paperen
dc.contributor.conferenceACM International Conference on Information and Knowledge Management (CIKM '16) (24 Oct 2016 - 28 Oct 2016 : Indianapolis, IN, USA)en
pubs.library.collectionComputer Science publicationsen
dc.identifier.orcidZhang, W. [0000-0002-0406-5974]en
Appears in Collections:Computer Science publications

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