TPSLVM: A dimensionality reduction algorithm based on thin plate splines

Xinwei Jiang, Junbin Gao, Tianjiang Wang, Daming Shi

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
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Dimensionality reduction (DR) has been considered as one of the most significant tools for data analysis. One type of DR algorithms is based on latent variable models (LVM). LVM-based models can handle the preimage problem easily. In this paper we propose a new LVM-based DR model, named thin plate spline latent variable model (TPSLVM). Compared to the well-known Gaussian process latent variable model (GPLVM), our proposed TPSLVM is more powerful especially when the dimensionality of the latent space is low. Also, TPSLVM is robust to shift and rotation. This paper investigates two extensions of TPSLVM, i.e., the back-constrained TPSLVM (BC-TPSLVM) and TPSLVM with dynamics (TPSLVM-DM) as well as their combination BC-TPSLVM-DM. Experimental results show that TPSLVM and its extensions provide better data visualization and more efficient dimensionality reduction compared to PCA, GPLVM, ISOMAP, etc.
Original languageEnglish
Pages (from-to)1795-1807
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number10
Publication statusPublished - 2014

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