Results 241 to 250 of about 660,377 (283)

Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset. [PDF]

Sci Data
Yu QS, Shan JY, Ma J, Gao G, Tao BZ, Qiao GY, Zhang JN, Wang T, Zhao YF, Qin XL, Yin YH.   +10 more
europepmc   +1 more source

Spatial domain identification method based on multi-view graph convolutional network and contrastive learning. [PDF]

PLoS Comput Biol
Liang X, Xiao S, Ba L, Feng Y, Ma Z, Adilova F, Qi J, Jin S.   +7 more
europepmc   +1 more source

MVIB-Lip: Multi-View Information Bottleneck for Visual Speech Recognition via Time Series Modeling. [PDF]

Entropy (Basel)
Li Y, Sun H, Cai J, Wu J.
europepmc   +1 more source

MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification. [PDF]

Brain Sci
Yin Z, Xu F, Ma Y, Huang S, Ren K, Zhang L.   +5 more
europepmc   +1 more source

Multi-view clustering via global-view graph learning. [PDF]

PLoS One
Li Q, Yang G.
europepmc   +1 more source

Multi-View Intact Space Learning [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
It is practical to assume that an individual view is unlikely to be sufficient for effective multi-view learning. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to ...
Chang Xu, Dacheng Tao
exaly   +4 more sources

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Multi-View Discriminant Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for ...
Meina, Kan, Shiguang, Shan, Haihong, Zhang, Shihong, Lao, Xilin, Chen   +4 more
openaire   +2 more sources

Binary Multi-View Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
Clustering is a long-standing important research problem, however, remains challenging when handling large-scale image data from diverse sources. In this paper, we present a novel Binary Multi-View Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data.
Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, Ling Shao   +4 more
openaire   +3 more sources

Contrastive Multi-View Kernel Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views into a single kernel.
Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Qing Liao, Yuanqing Xia   +4 more
openaire   +2 more sources

Sequential multi-view subspace clustering

Neural Networks, 2022
Self-representation based subspace learning has shown its effectiveness in many applications, but most existing methods do not consider the difference between different views. As a result, the learned self-representation matrix cannot well characterize the clustering structure.
Lei, Fangyuan, Li, Qin
openaire   +2 more sources