Double-fold localized multiple matrixized learning machine

Zhu, Changming; Wang, Zhe; Gao, Daqi; Feng, Xiang

doi:10.1016/j.ins.2014.10.024

Cited by 7 publications

(1 citation statement)

References 46 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While for nonlinearly ones which are ubiquitous, nonlinear classifiers including NCC [ 2 ], FC-NTD [ 3 ], KMHKS [ 4 ], KSVM [ 5 ] are more suitable. One kind of nonlinear classifiers is kernel-based ones including MultiV-KMHKS [ 6 ], MVMHKS [ 7 ], RMVMHKS [ 8 ], DLMMLM [ 9 ], UDLMMLM [ 10 ], etc [ 11 – 13 ] and they adopt kernel functions to generate kernel matrices firstly and get optimal classifier parameters after the solution of these matrices. Here, for convenience, we summary full names and abbreviations for some terms in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Improved linear classifier model with Nyström

Zhu¹,

Ji²,

Chao³

et al. 2018

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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.

show abstract