Results 41 to 50 of about 541,400 (289)

K-Subspace Clustering [PDF]

, 2009
The widely used K-means clustering deals with ball-shaped (spherical Gaussian) clusters. In this paper, we extend the K-means clustering to accommodate extended clusters in subspaces, such as line-shaped clusters, plane-shaped clusters, and ball-shaped clusters. The algorithm retains much of the K-means clustering flavors: easy to implement and fast to
Dingding Wang 0001, Chris H. Q. Ding, Tao Li 0001   +2 more
openaire   +2 more sources

A survey on soft subspace clustering [PDF]

Information Sciences, 2016
This paper has been published in Information Sciences Journal in ...
Zhaohong Deng, Kup-Sze Choi, Yizhang Jiang, Jun Wang 0024, Shitong Wang 0001   +4 more
openaire   +4 more sources

Subspace-based I-nice Clustering Algorithm [PDF]

Jisuanji kexue
Subspace clustering of high-dimensional data is a hot issue in the field of unsupervised learning.The difficulty of subspace clustering lies in finding the appropriate subspaces and corresponding clusters.At present,the most existing subspace clustering ...
HE Yifan, HE Yulin, CUI Laizhong, HUANG Zhexue
doaj   +1 more source

Cluster Evaluation of Density Based Subspace Clustering

, 2010
Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach.
Jasni, Mohamad Zain, Sembiring, Rahmat Widia   +1 more
core   +2 more sources

Hypergraph Convolutional Subspace Clustering With Multihop Aggregation for Hyperspectral Image

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Subspace clustering methods have become a powerful tool to cluster hyperspectral imaging (HSI) data as they ensure theoretical guarantees and empirical success.
Zijia Zhang, Yaoming Cai, Wenyin Gong, Pedram Ghamisi, Xiaobo Liu, Richard Gloaguen   +5 more
doaj   +1 more source

Latent Distribution Preserving Deep Subspace Clustering [PDF]

, 2019
Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is smaller than the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness
Liu, X, Jun Zhou, Zhou, Jun, Hancock, E, wang, dong, Lei Zhou, Wang, D, Liu, Xianglong, Xiao Bai, Edwin Hancock, Dong Wang, Xiao, Bai, Xianglong Liu, Zhou, Lei, Hancock, Edwin R, Zhou, J, Bai, X, Zhou, L   +17 more
core   +1 more source

Coping With New Challengens for Density-Based Clustering [PDF]

, 2004
Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.
Kröger, Peer
core   +1 more source

Greedy Subspace Clustering

CoRR, 2014
We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces.
Dohyung Park, Constantine Caramanis, Sujay Sanghavi   +2 more
openaire   +3 more sources

Discriminative Semantic Subspace Analysis for Relevance Feedback [PDF]

, 2016
Content-based image retrieval (CBIR) has attracted much attention during the past decades for its potential practical applications to image database management. A variety of relevance feedback (RF) schemes have been designed to bridge the gap between low-
Zhang, Lining, Lining Zhang, Shao, Ling, Shum, Hubert P. H., Ling Shao, Hubert P. H. Shum, Shum, Hubert   +6 more
core   +1 more source

Unsupervised Locality-Preserving Robust Latent Low-Rank Recovery-Based Subspace Clustering for Fault Diagnosis

IEEE Access, 2018
With the increasing demand for unsupervised learning for fault diagnosis, the subspace clustering has been considered as a promising technique enabling unsupervised fault diagnosis. Although various subspace clustering methods have been developed to deal
Jie Gao, Myeongsu Kang, Jing Tian, Lifeng Wu, Michael Pecht   +4 more
doaj   +1 more source