Results 31 to 40 of about 463,538 (280)

A Feature-Reduction Multi-View k-Means Clustering Algorithm

IEEE Access, 2019
The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas.
Miin-Shen Yang, Kristina P. Sinaga
doaj   +1 more source

A Novel Double-Index-Constrained, Multi-View, Fuzzy-Clustering Algorithm and its Application for Detecting Epilepsy Electroencephalogram Signals

IEEE Access, 2019
When processing a multi-view, epilepsy electroencephalogram (EEG) dataset, the traditional single-view clustering algorithms cannot fully mine the correlation information between each view and identify the importance of each view because of the ...
Jiaqi Zhu, Kang Li, Kaijian Xia, Xiaoqing Gu, Jing Xue, Shi Qiu, Yizhang Jiang, Pengjiang Qian   +7 more
doaj   +1 more source

Finding Optimal Views for 3D Face Shape Modeling [PDF]

, 2010
A fundamental problem in multi-view 3D face modeling is the determination of the set of optimal views (poses) required for accurate 3D shape estimation of a generic face.
Lee, Jinho, Machiraju, Raghu, Moghaddam, Baback, Pfister, Hanspeter   +3 more
core   +1 more source

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification [PDF]

, 2010
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level.
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Paul De Vos, Bernard De Baets, P Dawyndt, C Kunitsky, B Slabbinck, B Slabbinck, M Heyndrickx, P Kämpfer, DE Stead, M Vancanneyt, LG Wayne, E Stackebrandt, E Stackebrandt, KT Konstantinidis, KT Konstantinidis, E Pruesse, D Koller, A Mccallum, S Dumais, HP Kriegel, Z Barutcuoglu, N Cesa-Bianchi, J Rousu, C Vens, T Hofmann, F Wu, JS Lee, S Cheong, B Fei, R Craig, AC McHardy, N Diaz, SP Lapage, RR Sokal, N Saitou, J Felsenstein, J Felsenstein, P Dawyndt, R Kohavi, IH Witten, I Letunic, JP Euzéby, L Breiman, L Breiman, T Hastie, RO Duda, B Mirkin, B Slabbinck, BM Hansen, D Hutsebaut, JP Parker   +53 more
core   +3 more sources

Multi-View Clustering

, 2018
With a plethora of data capturing modalities becoming available, the same data object often leaves different kinds of digital footprints. This naturally leads to datasets comprising the same set of data objects represented in different forms, called multi-view data.
Padmanabhan, Deepak, Jurek-Loughrey, Anna   +1 more
openaire   +2 more sources

Multi-view Hierarchical Clustering

, 2020
This paper focuses on the multi-view clustering, which aims to promote clustering results with multi-view data. Usually, most existing works suffer from the issues of parameter selection and high computational complexity. To overcome these limitations, we propose a Multi-view Hierarchical Clustering (MHC), which partitions multi-view data recursively ...
Zheng, Qinghai, Zhu, Jihua, Ma, Shuangxun   +2 more
openaire   +2 more sources

AMCFCN: attentive multi-view contrastive fusion clustering net [PDF]

PeerJ Computer Science
Advances in deep learning have propelled the evolution of multi-view clustering techniques, which strive to obtain a view-common representation from multi-view datasets.
Huarun Xiao, Zhiyong Hong, Liping Xiong, Zhiqiang Zeng   +3 more
doaj   +2 more sources

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

An Integer Linear Programming Model for View Selection on Overlapping Camera Clusters [PDF]

, 2014
Multi-View Stereo (MVS) algorithms scale poorly on large image sets, and quickly become unfeasible to run on a single machine with limited memory. Typical solutions to lower the complexity include reducing the redundancy of the image set (view selection),
Gool, Luc Van, Leonardi, Riccardo, Mauro, Massimo, Riemenschneider, Hayko, Signoroni, Alberto   +4 more
core   +1 more source

Interpretable multi-view clustering

Pattern Recognition
Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains
Mudi Jiang, Lianyu Hu, Zengyou He, Zhikui Chen   +3 more
openaire   +2 more sources