Shape component analysis: Structure-preserving dimension reduction on biological shape spaces

Hao Chih Lee, Tao Liao, Yongjie Jessica Zhang, Ge Yang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Motivation: Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques. Results: We have developed a technique for non-linear dimension reduction of 2D and 3D biological shape representations on their Riemannian spaces. A key feature of this technique is that it preserves distances between different shapes in an embedded low-dimensional shape space. We demonstrate an application of this technique by combining it with non-linear mean-shift clustering on the Riemannian spaces for unsupervised clustering of shapes of cellular organelles and proteins.

Original languageEnglish
Pages (from-to)755-763
Number of pages9
Issue number5
StatePublished - 1 Mar 2016
Externally publishedYes


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