Improved linear classifier model with Nyström

Most data sets consist of interlaced-distributed samples from multiple classes and since these samples always cannot be classified correctly by a linear hyperplane, so we name them nonlinearly separable data sets and corresponding classifiers are named nonlinear classifiers. Traditional nonlinear cl...

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Bibliographic Details
Published in:PloS one 2018-11, Vol.13 (11), p.e0206798-e0206798
Main Authors: Zhu, Changming, Ji, Xiang, Chen, Chao, Zhou, Rigui, Wei, Lai, Zhang, Xiafen
Format: Article
Language:English
Subjects:
Algorithms
Archives & records
Artificial intelligence
Classification
Classification - methods
Classifiers
Clustering
Computer and Information Sciences
Computer applications
Computer Simulation
Data Interpretation, Statistical
Databases, Factual - statistics & numerical data
Datasets
Engineering
Engineering and Technology
Handwriting
Humans
Hyperplanes
International conferences
Kernel functions
Knowledge management
Linear Models
Medicine and Health Sciences
Methods
Nonlinear Dynamics
Pattern recognition
Physical Sciences
Research and Analysis Methods
Support Vector Machine
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Summary:Most data sets consist of interlaced-distributed samples from multiple classes and since these samples always cannot be classified correctly by a linear hyperplane, so we name them nonlinearly separable data sets and corresponding classifiers are named nonlinear classifiers. Traditional nonlinear classifiers adopt kernel functions to generate kernel matrices and then get optimal classifier parameters with the solution of these matrices. But computing and storing kernel matrices brings high computational and space complexities. Since INMKMHKS adopts Nyström approximation technique and NysCK changes nonlinearly separable data to linearly ones so as to reduce the complexities, we combines ideas of them to develop an improved NysCK (INysCK). Moreover, we extend INysCK into multi-view applications and propose multi-view INysCK (MINysCK). Related experiments validate the effectiveness of them in terms of accuracy, convergence, Rademacher complexity, etc.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0206798