Locality Preserving Projections for Grassmann Manifold

Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Haoran Chen, Muhammad Ali, Baocai Yin

Research output: Book chapter/Published conference paperConference paper

8 Citations (Scopus)
27 Downloads (Pure)

Abstract

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grass-mann based algorithms.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2893-2900
Number of pages8
ISBN (Electronic)9780999241103
Publication statusPublished - Aug 2017
Event26th International Joint Conference on Artificial Intelligence: IJCAI 2017 - Melbourne Convention and Exhibition Centre and RMIT Building 80, Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
https://ijcai-17.org/index.html (Conference website)
https://ijcai-17.org/colocated-events.html (Co-located events)

Conference

Conference26th International Joint Conference on Artificial Intelligence
CountryAustralia
CityMelbourne
Period19/08/1725/08/17
Internet address

Fingerprint Dive into the research topics of 'Locality Preserving Projections for Grassmann Manifold'. Together they form a unique fingerprint.

  • Cite this

    Wang, B., Hu, Y., Gao, J., Sun, Y., Chen, H., Ali, M., & Yin, B. (2017). Locality Preserving Projections for Grassmann Manifold. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) (pp. 2893-2900). International Joint Conferences on Artificial Intelligence. https://www.ijcai.org/proceedings/2017/0403.pdf