Product grassmann manifold representation and Its LRR models

Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin

Research output: Book chapter/Published conference paperConference paperpeer-review

10 Citations (Scopus)


It is a challenging problem to cluster multi-And highdimensional data with complex intrinsic properties and nonlinear manifold structure. The recently proposed subspace clustering method, Low Rank Representation (LRR), shows attractive performance on data clustering, but it generally does with data in Euclidean spaces. In this paper, we intend to cluster complex high dimensional data with multiple varying factors. We propose a novel representation, namely Product Grassmann Manifold (PGM), to represent these data. Additionally, we discuss the geometry metric of the manifold and expand the conventional LRR model in Euclidean space onto PGM and thus construct a new LRR model. Several clustering experimental results show that the proposed method obtains superior accuracy compared with the clustering methods on manifolds or conventional Euclidean spaces.
Original languageEnglish
Title of host publicationProceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Subtitle of host publicationAAAI-16
Place of PublicationCalifornia, USA
PublisherAAAI Press
Number of pages8
ISBN (Electronic)9781577357605
Publication statusPublished - 2016
EventThirtieth AAAI Conference on Artificial Intelligence (AAAI-16) - Phoenix Convention Center, Phoenix, United States
Duration: 12 Feb 201617 Feb 2016


ConferenceThirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
CountryUnited States
Internet address

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