Title :
Karhunen-Loeve Subspace
Author :
Ogawa, Hidemitsu
Author_Institution :
Dept. of Comput. Sci., Tokyo Inst. of Technol., Japan
fDate :
30 Aug-3 Sep 1992
Abstract :
The Karhunen-Loeve expansion is a well-known technique in the pattern recognition field, however, there is no exact proof of the K-L expansion in the context of approximation to a set of patterns. This paper provides an exact proof of the following well-known theorem: an N-dimensional subspace provides the best approximation to a set of patterns if and only if it is spanned by the first N-number of eigenelements of the covariance operator of the pattern set
Keywords :
approximation theory; eigenvalues and eigenfunctions; image recognition; Karhunen-Loeve expansion; approximation; multidimensional subspace; pattern recognition; Books; Computer science; Eigenvalues and eigenfunctions; Hilbert space; Image processing; Pattern recognition; Sufficient conditions;
Conference_Titel :
Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2915-0
DOI :
10.1109/ICPR.1992.201725