Results 101 to 110 of about 49,338 (223)

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

MoCo + ROVir: Synergy Between Respiratory Motion Compensation and Cardiac Receive Region Focusing for Cardiac MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To improve cardiac motion representation and reduce artifacts for cardiac‐ and respiratory‐resolved imaging through a synergistic combination of retrospective cardiac phased array RF focusing and rigid respiratory motion compensation (MoCo).
Zheyuan Hu, Hsu‐Lei Lee, Tianle Cao, Takegawa Yoshida, Lingceng Ma, J. Paul Finn, Kim‐Lien Nguyen, Anthony G. Christodoulou   +7 more
wiley   +1 more source

Discriminative variable selection for clustering with the sparse Fisher-EM algorithm

, 2011
The interest in variable selection for clustering has increased recently due to the growing need in clustering high-dimensional data. Variable selection allows in particular to ease both the clustering and the interpretation of the results.
Bouveyron, Charles, Brunet, Camille
core   +2 more sources

Free‐Running Three‐Dimensional Cardiac Extracellular Volume Mapping in a Single Scan With Mid‐Scan Contrast Injection

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a new method for free‐running three‐dimensional (3D) extracellular volume mapping of the heart in a single scan with mid‐scan contrast injection. Methods 3D cardiac MR imaging was performed with a single scan that acquired k‐space data continuously using an inversion recovery (IR) sequence with a spoiled gradient‐echo ...
Wonil Lee, Paul Kyu Han, Thibault Marin, Ismaël B. G. Mounime, Didi Chi, Felicitas J. Bijari, Marc D. Normandin, Georges El Fakhri, Chao Ma   +8 more
wiley   +1 more source

Electronic Correlation Effects in the Response of Graphene and MoS2 Monolayers to the Impact of Highly‐Charged Ions

physica status solidi (b), EarlyView.
The response of graphene and MoS2 monolayers to highly‐charged impinging ions is investigated using nonequilibrium Green functions theory. Electronic correlations are found to have a significantly stronger influence on the ultrafast ion‐induced electron dynamics in MoS2 than in graphene.
Giorgio Lovato, Michael Bonitz, Karsten Balzer, Fabio Caruso, Jan‐Philip Joost   +4 more
wiley   +1 more source

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

Sketched Subspace Clustering

IEEE Transactions on Signal Processing, 2018
The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly ...
Traganitis, Panagiotis A., Giannakis, Georgios B.   +1 more
openaire   +2 more sources

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

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, Chris Ding, Tao Li
openaire   +1 more source

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang, Jian Yang, Bob Zhang, Yang Tang, Wenli Du, Bihan Wen   +5 more
wiley   +1 more source