.png){kind=icon}
返回
A nonlinear kernel support matrix machine for matrix learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-018-0896-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In many problems of supervised tensor learning, real world data such as face images or MRI scans are naturally represented as matrices, which are also called as second order tensors. Most existing classifiers based on tensor representation, such as support tensor machine and kernelized support tensor machine need to solve iteratively which occupy much time and may suffer from local minima. In this paper, we present a kernel support matrix machine which performs a matrix-form inner product with maximum margin classifier. Specifically, the matrix inner product is introduced to leverage the inherent structural information within matrix data. Further, matrix kernel functions are applied to detect the nonlinear relationships. We analyze a unifying optimization problem for which we propose an asymptotically convergent algorithm. Theoretical analysis for the generalization bounds is derived based on Rademacher complexity with respect to a probability distribution. We demonstrate the merits of the proposed method by exhaustive experiments on both simulation study and a number of real-word datasets from a variety of application domains.
Keyword:
Kernel support matrix machine
Supervised tensor learning
Reproducing kernel matrix
Hilbert space
Kernel functions
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
分享收藏分享收藏Preparation of water-dispersible TiO2 nanoparticles from titanium tetrachloride using urea hydrogen peroxide as an oxygen donor.png)
CrystEngComm
OAAI
CrystEngCommIF0
OAAI分享收藏
查看更多内容