Results 211 to 220 of about 86,218 (261)

Robust 3D Multi-Object Tracking via 4D mmWave Radar-Camera Fusion and Disparity-Domain Depth Recovery. [PDF]

Sensors (Basel)
Xie Y, Li X, Wang D, Wang Z, Li S, Wang J, Sun Z.   +6 more
europepmc   +1 more source

Cross-modality representation and multi-sample integration of spatially resolved omics data. [PDF]

Brief Bioinform
Li Z, Cui X, Chen X, Gao Z, Liu Y, Pan Y, Chen S, Lv H, Zhai L, Jiang R.   +9 more
europepmc   +1 more source

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Multi-View Clustering

Fourth IEEE International Conference on Data Mining (ICDM'04), 2005
We consider clustering problems in which the available attributes can be split into two independent subsets, such that either subset suffices for learning. Example applications of this multi-view setting include clustering of Web pages which have an intrinsic view (the pages themselves) and an extrinsic view (e.g., anchor texts of inbound hyperlinks ...
Steffen Bickel, Tobias Scheffer
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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 0006, Li Liu 0004, Fumin Shen, Heng Tao Shen, Ling Shao 0001   +4 more
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Dependence-Guided Multi-View Clustering

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
In this paper, we propose a novel approach called dependence-guided multi-view clustering (DGMC). Our model enhances the dependence between unified embedding learning and clustering, as well as promotes the dependence between unified embedding and embedding of each view.
Xia Dong, Danyang Wu, Feiping Nie 0001, Rong Wang 0001, Xuelong Li 0001   +4 more
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Partial Multi-View Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2014
Real data are often with multiple modalities or comingfrom multiple channels, while multi-view clusteringprovides a natural formulation for generating clustersfrom such data. Previous studies assumed that each exampleappears in all views, or at least there is one viewcontaining all examples.
Shao-Yuan Li, Yuan Jiang 0001, Zhi-Hua Zhou   +2 more
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Reliable Multi-View Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
With the advent of multi-view data, multi-view learning (MVL) has become an important research direction in machine learning. It is usually expected that multi-view algorithms can obtain better performance than that of merely using a single view.
Hong Tao, Chenping Hou, Xinwang Liu 0002, Tongliang Liu, Dongyun Yi, Jubo Zhu   +5 more
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Collaborative multi-view clustering

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
The purpose of this article is to introduce a new collaborative multi-view clustering approach based on a probabilistic model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques.
Mohamad Ghassany, Nistor Grozavu, Younès Bennani   +2 more
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