.png){kind=icon}
返回
Learning Low-Dimensional Latent Graph Structures: A Density Estimation Approach
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2019.2917696.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We aim to automatically learn a latent graph structure in a low-dimensional space from high-dimensional, unsupervised data based on a unified density estimation framework for both feature extraction and feature selection, where the latent structure is considered as a compact and informative representation of the high-dimensional data. Based on this framework, two novel methods are proposed with very different but intuitive learning criteria from existing methods. The proposed feature extraction method can learn a set of embedded points in a low-dimensional space by naturally integrating the discriminative information of the input data with structure learning so that multiple disconnected embedding structures of data can be uncovered. The proposed feature selection method preserves the pairwise distances only on the optimal set of features and selects these features simultaneously. It not only obtains the optimal set of features but also learns both the structure and embeddings for visualization. Extensive experiments demonstrate that our proposed methods can achieve competitive quantitative (often better) results in terms of discriminant evaluation performance and are able to obtain the embeddings of smooth skeleton structures and select optimal features to unveil the correct graph structures of high-dimensional data sets.
Keyword:
Feature extraction
Manifolds
Data models
Dimensionality reduction
Kernel
Data visualization
Noise measurement
Density estimation
feature selection
structure learning
unsupervised dimensionality reduction
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:
8.9
论文数:
7.5K
被引数:
7.2W
机构
引用论文
A unified probabilistic framework for robust manifold learning and embedding.png)
MACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification用于图像聚类和半监督分类的自动加权多视图学习
PREAI
分享收藏Mechanical characterization of neural electrodes based on PDMS-parylene C-PDMS sandwiched system
PREAI
分享收藏
查看更多内容