TPSLVM: a dimensionality reduction algorithm based on thin plate splines

Article


Jiang, X., Gao, J., Wang, T. and Shi, D. 2014. TPSLVM: a dimensionality reduction algorithm based on thin plate splines. IEEE Transactions on Cybernetics. 44 (10), pp. 1795-1807. https://doi.org/10.1109/TCYB.2013.2295329
TypeArticle
TitleTPSLVM: a dimensionality reduction algorithm based on thin plate splines
AuthorsJiang, X., Gao, J., Wang, T. and Shi, D.
Abstract

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.

Research GroupArtificial Intelligence group
PublisherIEEE
JournalIEEE Transactions on Cybernetics
ISSN2168-2267
Electronic2168-2275
Publication dates
Print01 Oct 2014
Publication process dates
Deposited03 Jun 2015
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1109/TCYB.2013.2295329
LanguageEnglish
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