.png){kind=icon}
返回
Multi-view hypergraph learning by patch alignment framework
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2013.02.017.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph-based methods are currently popular for dimensionality reduction. However, most of them suffer from over-simplified assumption of pairwise relationships among data. Especially for multi-view data, different relationships from different views are hard to be integrated into a single graph. In this paper, we propose a novel semi-supervised dimensionality reduction method for multi-view data. First, we assume the hyperedges in hypergraph as patches and apply hypergraph to the patch alignment framework. Second, the weights of the hyperedges are computed with statistics of distances between neighboring pairs and the patches from different views are integrated. In this way, we construct Multi-view Hypergraph Laplacian matrix and we get the dimensionality-reduced data by solving the standard eigen-decomposition to obtain the projection matrix. The experimental results demonstrate the effectiveness of the proposed method on retrieval performance. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Dimensionality reduction
Hypergraph
Multi-view learning
Patch alignment framework
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Principal manifolds and nonlinear dimensionality reduction via tangent space alignment基于切线空间对齐的主流形和非线性降维
OAAI
OAAI分享收藏分享收藏分享收藏Tinnitus Retraining Therapy (TRT) as a Method for Treatment of Tinnitus and Hyperacusis Patients耳鸣再训练疗法 (TRT) 作为治疗耳鸣和高亢患者的方法
PREAI
分享收藏Pairwise constraints based multiview features fusion for scene classification.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容