Results 91 to 100 of about 9,565 (204)

CUR Decompositions, Similarity Matrices, and Subspace Clustering

Frontiers in Applied Mathematics and Statistics, 2019
A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces U=⋃Mi=1Si.
Akram Aldroubi, Keaton Hamm, Ahmet Bugra Koku, Ali Sekmen   +3 more
doaj   +1 more source

Block-Constraint Laplacian-Regularized Low-Rank Representation and Its Application for Cancer Sample Clustering Based on Integrated TCGA Data

Complexity, 2020
Low-Rank Representation (LRR) is a powerful subspace clustering method because of its successful learning of low-dimensional subspace of data. With the breakthrough of “OMics” technology, many LRR-based methods have been proposed and used to cancer ...
Juan Wang, Jin-Xing Liu, Chun-Hou Zheng, Cong-Hai Lu, Ling-Yun Dai, Xiang-Zhen Kong   +5 more
doaj   +1 more source

Subspace Clustering with Active Learning [PDF]

, 2019
Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion segmentation and face clustering.
Nicos G. Pavlidis, Peng, Hankui, Pavlidis, Nicos, Hankui Peng   +3 more
core   +1 more source

Finding Hierarchies of Subspace Clusters [PDF]

, 2006
Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace ...
Elke Achtert, Christian Böhm 0001, Hans-Peter Kriegel, Peer Kröger, Ina Müller-Gorman, Arthur Zimek   +5 more
openaire   +1 more source

Algebraic Clustering of Affine Subspaces

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018
Subspace clustering is an important problem in machine learning with many applications in computer vision and pattern recognition. Prior work has studied this problem using algebraic, iterative, statistical, low-rank and sparse representation techniques. While these methods have been applied to both linear and affine subspaces, theoretical results have
Manolis C. Tsakiris, René Vidal
openaire   +3 more sources

scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data. [PDF]

PLoS Comput Biol, 2022
Wang H, Zhao J, Zheng C, Su Y.
europepmc   +1 more source

Multi-view clustering via consensus coefficient matrix and separate segmentation matrices

Journal of Information and Telecommunication
In recent years, achieving data from different sources and different views has caused to have many multi-view data sets. Among multi-view learning methods, multi-view clustering has been considered as an appropriate method to analyse these data by many ...
Fatemeh Sadjadi, Mina Jamshidi, Amir Asadi   +2 more
doaj   +1 more source

Preserving bilateral view structural information for subspace clustering. [PDF]

Knowl Based Syst, 2022
Peng C, Zhang J, Chen Y, Xing X, Chen C, Kang Z, Guo L, Cheng Q.   +7 more
europepmc   +1 more source

Subspace K-means clustering

Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E., Ceulemans, Eva, De Roover, Kim, Van Leeuwen, Karla   +3 more
openaire   +2 more sources

A self-training subspace clustering algorithm based on adaptive confidence for gene expression data. [PDF]

Front Genet, 2023
Li D, Liang H, Qin P, Wang J.
europepmc   +1 more source