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Discriminant locality preserving projection on Grassmann Manifold for image-set classification

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Abstract

In the domain of image-set analysis, the Grassmann Manifold serves as an efficacious instrument, though the computational expenses it incurs are notably substantial. Locality Preserving Projection on Grassmann Manifold (GLPP), a classical algorithm for dimensionality reduction, effectively alleviates these computational burdens. However, it is important to note that the construction of the neighborhood graph in GLPP overlooks the categorical information of the data, thereby limiting its discriminative capacity. To address this issue, we propose the Discriminant Locality Preserving Projection on Grassmann Manifold (GDLPP) and Semi-Supervised Locality Preserving Projection on Grassmann Manifold (GSLPP). Classification outcomes across 6 image-set datasets reveal that the classification capabilities of GDLPP and GSLPP surpass those of existing image-set classification algorithms. By effectively preserving the local manifold structure of the data and comprehensively leveraging the label information, GDLPP and GSLPP notably augment the feature extraction and classification prowess of GLPP.

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Acknowledgements

This work was supported by Chongqing Municipal Education Commission under grant KJZD-K202100505, Chongqing Science and Technology Bureau under grant cstc2020jscx-msxmX0190, the Ministry of Education of China under grant 20YJAZH084.

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  1. Benchao Li and Ting Wang have contributed equally to this work.

Authors and Affiliations

  1. College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China

    Benchao Li, Ting Wang & Ruisheng Ran

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  1. Benchao Li
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  2. Ting Wang
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Contributions

Benchao Li contributed to software and writing—original draft. Ting Wang contributed to methodology, writing—review & editing, and visualization. Ruisheng Ran contributed to conceptualization and supervision.

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Correspondence to Ruisheng Ran.

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Li, B., Wang, T. & Ran, R. Discriminant locality preserving projection on Grassmann Manifold for image-set classification. J Supercomput 81, 397 (2025). https://doi.org/10.1007/s11227-024-06904-1

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