.png){kind=icon}
返回
A unified framework for semi-supervised dimensionality reduction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2008.01.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In practice, many applications require a dimensionality reduction method to deal with the partially labeled problem. In this paper, we propose a semi-supervised dimensionality reduction framework, which can efficiently handle the unlabeled data. Under the framework, several classical methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), maximum margin criterion (MMC), locality preserving projections (LPP) and their corresponding kernel versions can be seen as special cases. For high-dimensional data, we can give a low-dimensional embedding result for both discriminating multi-class sub-manifolds and preserving local manifold structure. Experiments show that our algorithms can significantly improve the accuracy rates of the corresponding supervised and unsupervised approaches. (c) 2008 Elsevier Ltd. All rights reserved.
Keyword:
dimensionality reduction
discriminant analysis
manifold analysis
semi-supervised learning
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
Effects of stereoisomers of estradiol on food intake, body weight and hoarding behavior in female rats雌二醇立体异构体对雌性大鼠摄食、体重和囤积行为的影响
PREAI
分享收藏Evaluation of charge transfer resistance by geometrical extrapolation of the centre of semicircular impedance diagrams通过半圆阻抗图中心的几何外推法评估电荷转移电阻
PREAI
分享收藏Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏
查看更多内容