.png){kind=icon}
返回
Multi-View Concept Learning for Data Representation
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2015.2448542.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Real-world datasets often involve multiple views of data items, e.g., a Web page can be described by both its content and anchor texts of hyperlinks leading to it; photos in Flickr could be characterized by visual features, as well as user contributed tags. Different views provide information complementary to each other. Synthesizing multi-view features can lead to a comprehensive description of the data items, which could benefit many data analytic applications. Unfortunately, the simple idea of concatenating different feature vectors ignores statistical properties of each view and usually incurs the curse of dimensionality problem. We propose Multi-view Concept Learning (MCL), a novel nonnegative latent representation learning algorithm for capturing conceptual factors from multi-view data. MCL exploits both multi-view information and label information. The key idea is to learn a common latent space across different views which (1) captures the semantic relationships between data items through graph embedding regularization on labeled items, and (2) allows each latent factor to be associated with a subset of views via sparseness constraints. In this way, MCL could capture flexible conceptual patterns hidden in multi-view features. Experiments on a toy problem and three real-world datasets show that MCL performs well and outperforms baseline methods.
Keyword:
Multi-view learning
nonnegative matrix factorization
graph embedding
structured sparsity
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:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
分享收藏Quantitative Performance Evaluation of Uncertainty-Aware Hybrid AADL Designs Using Statistical Model Checking使用统计模型检查对不确定性感知的混合AADL设计进行定量性能评估
OAAI
OAAI分享收藏
查看更多内容