Paper Info
Title
Using Feature Correlation Measurement to Improve the Kernel Minimum Squared Error Algorithm.
Abstract
The kernel minimum squared error (KMSE) is less computationally efficient when applied to large datasets. In this paper, we propose IKMSE, an algorithm which improves the computational efficiency of KMSE by using just a part of the training set, key nodes, as a certain linear combination of key nodes in the feature space can be used to approximate the discriminant vector. Our algorithm includes three steps. The first step is to measure the correlation between the column vectors in the kernel matrix, known as the feature correlation, using the cosine distance between them. The second step is to determine the key nodes using the following requirement: two arbitrary column vectors of the kernel matrix that correspond to the key nodes should have a small cosine distance value. In the third step, we use the key nodes to construct the KMSE model and classify the testing samples. There are usually many fewer key nodes than training samples and this is the basis of producing the efficiency of feature extraction in our method. Experimental results show that our improved method has low computational complexity as well as high classification accuracy.
Year
DOI
Venue
2016
10.1007/978-981-10-3002-4_46
Communications in Computer and Information Science
Keywords
Field
DocType
Kernel minimum squared error (KMSE),Feature extraction,Feature correlation measurement,Cosine distance
Kernel (linear algebra),Linear combination,Feature vector,Radial basis function kernel,Pattern recognition,Computer science,Algorithm,Mean squared error,Feature extraction,Artificial intelligence,Variable kernel density estimation,Computational complexity theory
Conference
Volume
ISSN
Citations 
662
1865-0929
0
PageRank 
References 
Authors
0.34
16
2
Name
Order
Citations
PageRank
Zizhu Fan132914.61
Zuoyong Li200.34