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In this paper we propose a novel methodology that extends Linear Discriminant Analysis (LDA) to Kendall’s shape space to classify 3D shapes and analyze which features most influence class differentiation. Our approach adapts LDA to the non-Euclidean geometry of the shape space, generalizing assumptions about the probability distribution of data in Euclidean spaces and incorporating parallel transport to improve the estimation of shape variability between clusters. A simulation study is performed to show the effectiveness of the proposed methodology.

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Intrinsic LDA for 3D Shape Classification via Parallel Transport

  • 会议论文

  • 发表日期：

    2026年

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  • Jorge Valero,
  • Vicent Gimeno i Garcia,
  • M. Victoria Ibáñez,
  • Amelia Simó

摘要

In this paper we propose a novel methodology that extends Linear Discriminant Analysis (LDA) to Kendall’s shape space to classify 3D shapes and analyze which features most influence class differentiation. Our approach adapts LDA to the non-Euclidean geometry of the shape space, generalizing assumptions about the probability distribution of data in Euclidean spaces and incorporating parallel transport to improve the estimation of shape variability between clusters. A simulation study is performed to show the effectiveness of the proposed methodology.