# Learning SVM Classifiers with Indefinite Kernels

@inproceedings{Gu2012LearningSC, title={Learning SVM Classifiers with Indefinite Kernels}, author={Suicheng Gu and Yuhong Guo}, booktitle={AAAI}, year={2012} }

Recently, training support vector machines with indefinite kernels has attracted great attention in the machine learning community. In this paper, we tackle this problem by formulating a joint optimization model over SVM classifications and kernel principal component analysis. We first reformulate the kernel principal component analysis as a general kernel transformation framework, and then incorporate it into the SVM classification to formulate a joint optimization model. The proposed model…

## 32 Citations

Indefinite kernels in least squares support vector machines and principal component analysis

- Computer Science
- 2017

A Primal Framework for Indefinite Kernel Learning

- Computer ScienceNeural Processing Letters
- 2019

A novel framework for indefinite kernel learning derived directly from the primal of SVM is presented, which establishes several new models not only for single indefinite kernel but also extends to multiple indefinite kernel scenarios.

SVM in Krĕın spaces

- Computer Science
- 2013

A new SVM approach that deals directly with indefinite kernels, and embraces the underlying idea that the negative part of an indefinite kernel may contain valuable information, and proposes two practical algorithms that outperform the approaches that modify the kernel.

University of Birmingham Indefinite Core Vector Machine

- Computer Science
- 2017

Experiments show that the derived iCVM solver is similar efficient as the Krĕin space Support Vector Machine but with substantially lower costs, such that also large scale problems can be processed.

A maximum margin clustering algorithm based on indefinite kernels

- Computer ScienceFrontiers of Computer Science
- 2018

This paper proposes a novel indefinite kernel clustering algorithm termed as indefinite kernel maximum margin clustering (IKMMC) based on the state-of-the-art MMC model, which embeds a new F-norm regularizer in the objective function to measure the diversity of the two kernels.

Unconstrained optimization in projection method for indefinite SVMs

- Computer Science2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- 2016

A projection matrix on indefinite kernels to formulate a positive semi-definite model that can be regarded as a generalized version of the spectrum method by varying parameter λ and can be obtained using unconstrained optimization.

Discriminality-driven regularization framework for indefinite kernel machine

- Computer ScienceNeurocomputing
- 2014

PROJECTION METHOD FOR SUPPORT VECTOR MACHINES WITH INDEFINITE KERNELS

- Computer Science
- 2015

The proposed model can be regarded as a generalized version of spectrum method (denoising method and flipping method) by varying parameter λ in projection matrix to formulate a positive semidefinite kernel.

A primal perspective for indefinite kernel SVM problem

- Computer ScienceFrontiers of Computer Science
- 2019

A primal perspective for solving the problem of indefinite kernel support vector machine is proposed and a novel algorithm termed as IKSVM-DC, which decomposes the primal function into the subtraction of two convex functions as a difference of conveX functions (DC) programming is presented.

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