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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| Veröffentlicht in: | PloS one 2018-11, Vol.13 (11), p.e0206798-e0206798 |
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| creator | Zhu, Changming Ji, Xiang Chen, Chao Zhou, Rigui Wei, Lai Zhang, Xiafen |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0206798 |
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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30395624</pmid><doi>10.1371/journal.pone.0206798</doi><orcidid>https://orcid.org/0000-0001-8039-5601</orcidid><oa>free_for_read</oa></addata></record> |
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| 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 |
| title | Improved linear classifier model with Nyström |
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