000163272 001__ 163272
000163272 005__ 20190316235028.0
000163272 02470 $$2ISI$$a000296062401055
000163272 037__ $$aCONF
000163272 245__ $$aApproximation of Pattern Transformation Manifolds with Parametric Dictionaries
000163272 269__ $$a2011
000163272 260__ $$c2011
000163272 336__ $$aConference Papers
000163272 520__ $$aThe construction of low-dimensional models explaining high-dimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern transformation manifold models generated by in-plane geometric transformations of 2D visual patterns. We propose a method for computing a manifold by building a representative pattern such that its transformation manifold accurately fits a set of given observations. We present a solution for the progressive construction of the representative pattern with the aid of a parametric dictionary, which in turn provides an analytical representation of the data and the manifold. Experimental results show that the patterns learned with the proposed algorithm can efficiently capture the main characteristics of the input data with high approximation accuracy, where the invariance to the geometric transformations of the data is accomplished due to the transformation manifold model.
000163272 6531_ $$aPattern transformation manifolds
000163272 6531_ $$amanifold learning
000163272 6531_ $$adimensionality reduction
000163272 6531_ $$amatching pursuit
000163272 6531_ $$asparse representations
000163272 6531_ $$aLTS4
000163272 700__ $$0242951$$aVural, Elif$$g185439
000163272 700__ $$0241061$$aFrossard, Pascal$$g101475
000163272 7112_ $$aIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)$$cPrague, Czech Republic$$dMay 22-27, 2011
000163272 773__ $$q977-980$$tIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
000163272 8564_ $$s188102$$uhttps://infoscience.epfl.ch/record/163272/files/LearningPTM.pdf$$yn/a$$zn/a
000163272 909C0 $$0252393$$pLTS4$$xU10851
000163272 909CO $$ooai:infoscience.tind.io:163272$$pconf$$pSTI$$qGLOBAL_SET
000163272 917Z8 $$x185439
000163272 917Z8 $$x101475
000163272 917Z8 $$x101475
000163272 937__ $$aEPFL-CONF-163272
000163272 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000163272 980__ $$aCONF