.png){kind=icon}
返回
Learning Manifold Structures With Subspace Segmentations
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2019.2895497.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Manifold learning has been widely used for dimensionality reduction and feature extraction of data recently. However, in the application of the related algorithms, it often suffers from noisy or unreliable data problems. For example, when the sample data have complex background, occlusions, and/or illuminations, the clustering of data is still a challenging task. To address these issues, we propose a family of novel algorithms for manifold regularized non-negative matrix factorization in this paper. In the algorithms, based on the alpha-beta-divergences, graph regularization with multiple segments is utilized to constrain the data transitivity in data decomposition. By adjusting two tuning parameters, we show that the proposed algorithms can significantly improve the robustness with respect to the images with complex background. The efficiency of the proposed algorithms is confirmed by the experiments on four different datasets. For different initializations and datasets, variations of cost functions and decomposition data elements in the learning are presented to show the convergent properties of the algorithms.
Keyword:
Clustering algorithms
Machine learning algorithms
Manifolds
Feature extraction
Linear programming
Dimensionality reduction
Matrix decomposition
Alpha– beta-divergence (AB-divergence)
convergence analysis
feature extraction
manifold regularization
non-negative matrix factorization (NMF)
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Twinning in Hexagonal Close-Packed Materials: The Role of Phase Transformation六方密堆积材料中的孪晶: 相变的作用.png)
Metals
OAAI
MetalsIF0
OAAI分享收藏Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression propertiesRSC Advances
RSC AdvancesIF0
PREAI
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
查看更多内容