.png){kind=icon}
返回
Image clustering by hyper-graph regularized non-negative matrix factorization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2014.01.043.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Image clustering is a critical step for the applications of content-based image retrieval, image annotation and other high-level image processing. To achieve these tasks, it is essential to obtain proper representation of the images. Non-negative Matrix Factorization (NMF) learns a part-based representation of the data, which is in accordance with how the brain recognizes objects. Due to its psychological and physiological interpretation, NMF has been successfully applied in a wide range of application such as pattern recognition, image processing and computer vision. On the other hand, manifold learning methods discover intrinsic geometrical structure of the high dimension data space. Incorporating manifold regularizer to standard NMF framework leads to novel performance. In this paper, we proposed a novel algorithm, call Hyper-graph regularized Non-negative Matrix Factorization (HNMF) for this purpose. HNMF captures intrinsic geometrical structure by constructing a hyper-graph instead of a simple graph. Hyper-graph model considers high-order relationship of samples and outperforms simple graph model. Empirical experiments demonstrate the effectiveness of the proposed algorithm in comparison to the state-of-the-art algorithms, especially some related works based on NMF. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Non-negative matrix factorization
Hyper-graph laplacian
Image clustering
Dimension reduction
Manifold regularization
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:
6.5
论文数:
2.5W
被引数:
6.5W
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LF-EME: Local features with elastic manifold embedding for human action recognitionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Pairwise constraints based multiview features fusion for scene classificationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容