Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8265
Title: Multilinear Isometric Embedding for visual pattern analysis
Authors: Liu, Y 
Liu, Y
Chan, CC 
Keywords: Data reduction
Geometry
Image processing
Learning (artificial intelligence)
Tensors
Issue Date: 2009
Publisher: IEEE
Source: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), September 27, 2009-October 4, 2009, Kyoto, p. 212-218 How to cite?
Abstract: This paper proposes a novel tensor based dimensionality reduction algorithm called Multilinear Isometric Embedding (MIE) based on a representative manifold learning algorithm Isomap. Unlike Isomap that unfolds input data to the vector form, MIE directly works on more general tensor representation and utilizes iterative strategy to seek the low-dimensional equivalence, which best preserves the global geometry. By avoiding the problems caused by data vectorization, MIE reduces the data analysis difficulty and computational cost. More importantly, MIE keeps the intrinsic tensor structure of the data in low-dimensional representation. Meanwhile, MIE inherits the merits of Isomap, i.e., the ability of uncovering the global geometry of high-dimensional observations. By providing explicit embedding function, MIE makes the embedding of new data points to the low-dimensional space straightforward. Experiments on various datasets validate the effectiveness of proposed method.
URI: http://hdl.handle.net/10397/8265
ISBN: 978-1-4244-4442-7
978-1-4244-4441-0 (E-ISBN)
DOI: 10.1109/ICCVW.2009.5457696
Appears in Collections:Conference Paper

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