.png){kind=icon}
返回
Higher rank Support Tensor Machines for visual recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2012.04.033.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work addresses the two class classification problem within the tensor-based large margin classification paradigm. To this end, we formulate the higher rank Support Tensor Machines (STMs), in which the parameters defining the separating hyperplane form a tensor (tensorplane) that is constrained to be the sum of rank one tensors. Subsequently, we propose two extensions in which the separating tensorplanes take into consideration the spread of the training data along the different tensor modes. More specifically, we first propose the higher rank Sigma/Sigma(w) STMs that use the total or the within-class covariance matrix in order to whiten the data and thus provide invariance to affine transformations. Second, we propose the higher rank Relative Margin Support Tensor Machines (RMSTMs) that bound from above the distance of the data samples from the separating tensorplane while maximizing the margin from it. The corresponding optimization problem is solved in an iterative manner utilizing the CANDECOMP/PARAFAC (CP) decomposition, where at each iteration the parameters corresponding to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine (SVM)-type optimization problem. The efficiency of the proposed method is illustrated on the problems of gait and action recognition where we report results that improve, in some cases considerably, the state of the art. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Support Tensor Machines
CANDECOMP/PARAFAC tensor decomposition
Relative Margin Support Vector Machines
Action recognition
Gait recognition
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:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Tracking disease progression by searching paths in a temporal network of biological processes.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Human action recognition using shape and CLG-motion flow from multi-view image sequencesPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容