NDLI: Higher-order random projection for tensor object recognition

Content Provider	IEEE Xplore Digital Library
Author	Sanguansat, P.
Copyright Year	2010
Description	Author affiliation: Rangsit University, Thailand (Sanguansat, P.)
Abstract	In this paper, the higher-order random projection (HORP) is proposed to directly project the higher-order tensor object from high-dimensional space to low-dimensional space for recognition task. In traditional random projection framework, the projection matrix does not depend on the training data hence it can avoid the principal classification problems such as over-fitting, Small Sample Size (SSS), and singularity problems. However, the tensor object must be transformed to vectors before projection. In this way, the size of projection matrix will depend on the product of all dimensions in all orders, that is very large and consumes lots of memory and computation time to process. Instead of the traditional projection, our method uses n-mode projection for a tensor object directly, which applies the random projection matrices to matrix unfolding in each mode simultaneously. Thus, the size of each projection matrix will depend on only a dimension of each order. The memory and computation time of this method will be substantially reduced. After projection, we investigate the results of HORP by the nearest neighbor classifier. Our experiments on well-known face databases demonstrate the significant of our proposed method.
Starting Page	615
Ending Page	619
File Size	607300
Page Count	5
File Format	PDF
ISBN	9781424470075
e-ISBN	9781424470105
e-ISBN	9781424470099
DOI	10.1109/ISCIT.2010.5665064
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-26
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Tensile stress Databases Face recognition Face Sparse matrices Principal component analysis
Content Type	Text
Resource Type	Article

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