DocumentCode :
445803
Title :
Data-dependent kernels for high-dimensional data classification
Author :
Wang, Jingdong ; Kwok, James T. ; Shen, Helen C. ; Quan, Long
Author_Institution :
Dept. of Comput. Sci., Hong Kong Univ. of Sci. & Technol., Clear Water Bay, China
Volume :
1
fYear :
2005
fDate :
31 July-4 Aug. 2005
Firstpage :
102
Abstract :
For high-dimensional data classification problems such as face recognition, one of the most efficient classifiers is the nearest neighbor (NN) classifier. What mostly affects the NN classification performance is the feature extracted by some methods. And the kernel method is one of the efficient methods for extracting features. However, the selection of kernel parameters is still difficult. In this paper, we propose a so-called data dependent kernel (DDK) which is defined by generalizing the Gaussian kernel. Also an efficient and practical method is presented to calculate the DDK parameters. Moreover, one DDK based on subspaces is given to improve the recognition performance. Experiments show that the proposed DDK can achieve promising classification performance in face recognition and SPECT heart diagnosis.
Keywords :
Gaussian processes; feature extraction; pattern classification; DDK parameters; Gaussian kernel; SPECT heart diagnosis; data-dependent kernels; face recognition; feature extraction; high-dimensional data classification; kernel method; kernel parameters; nearest neighbor classifier; Computer science; Data mining; Face recognition; Feature extraction; Independent component analysis; Kernel; Linear discriminant analysis; Nearest neighbor searches; Neural networks; Principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
Type :
conf
DOI :
10.1109/IJCNN.2005.1555813
Filename :
1555813
Link To Document :
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