.png){kind=icon}
返回
Flexible constrained sparsity preserving embedding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2016.06.027.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, two semi-supervised embedding methods are proposed, namely Constrained Sparsity Preserving Embedding (CSPE) and Flexible Constrained Sparsity Preserving Embedding (FCSPE). CSPE is a semi-supervised embedding method which can be considered as a semi-supervised extension of Sparsity Preserving Projections (SPP) integrated with the idea of in-class constraints. Both the labeled and unlabeled data can be utilized within the CSPE framework. However, CSPE does not have an out-of-sample extension since the projection of the unseen samples cannot be obtained directly. In order to have an inductive semi-supervised learning, i.e. being able to handle unseen samples, we propose FCSPE which can simultaneously provide a non-linear embedding and an approximate linear projection in one regression function. FCSPE simultaneously achieves the following: (i) the local sparse structures is preserved, (ii) the data samples with a same label are mapped onto one point in the projection space, and (iii) a linear projection that is the closest one to the non-linear embedding is estimated. Experimental results on eight public image data sets demonstrate the effectiveness of the proposed methods as well as their superiority to many competitive semi-supervised embedding techniques. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Constrained embedding
Sparsity preserving projections
Flexible manifold embedding
Semi-supervised learning
Out-of-sample problem
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
Local margin based semi-supervised discriminant embedding for visual recognition.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Graph-based semi-supervised learning with Local Binary Patterns for holistic object categorization基于局部二进制模式的基于图的半监督学习，用于整体对象分类
PREAI
分享收藏
查看更多内容