Results 21 to 30 of about 181,047 (259)
Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation
IET Computer Vision, 2021 In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally.
Liang Zhao +3 more
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Unsupervised Multi-view Learning [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Unsupervised multi-view learning is a hot research topic. The main challenge lies in how to integrate information from different views to enhance the unsupervised learning performance. In this paper, we present our research works on multi-view data clustering and multi-view network community detection respectively. The main contributions are summarized
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Low‐rank constrained weighted discriminative regression for multi‐view feature learning
CAAI Transactions on Intelligence Technology, 2021 In recent years, multi‐view learning has attracted much attention in the fields of data mining, knowledge discovery and machine learning, and been widely used in classification, clustering and information retrieval, and so forth. A new supervised feature
Chao Zhang, Huaxiong Li
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Learning for Multi-view Tracking
, 2023 In response to the growing trend towards end-to-end learning, we propose a novel framework advancing towards an end-to-end multi-camera multi-object tracking (MC-MOT) solution that addresses challenges like occlusions, viewpoint variations, and illumination changes.
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Multi‐view learning for benign epilepsy with centrotemporal spikes
IET Computer Vision, 2019 Benign epilepsy with centrotemporal spikes (BECT) may be the most popular epilepsy to attack children. In recent years, more and more studies have shown that magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) are promising ...
Ming Yan +3 more
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Multiview clustering method for view-unaligned data
Tongxin xuebao, 2022 A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous
Ao LI +5 more
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Deep Multi-View Concept Learning [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Multi-view data is common in real-world datasets, where different views describe distinct perspectives. To better summarize the consistent and complementary information in multi-view data, researchers have proposed various multi-view representation learning algorithms, typically based on factorization models. However, most previous methods were focused
Cai Xu +5 more
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Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier
IEEE Access, 2019 Multi-view data represented in multiple views contains more complementary information than a single view, whereby multi-view learning explores and utilizes the multi-view data.
Ting Shu, Bob Zhang, Yuan Yan Tang
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Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations Learning
IEEE Access, 2020 Spectral cluster based on multi-view data has proven effective for clustering multi-source real-world data because consensus and complementary information of multi-view data ensure the result of clustering.
Lijuan Wang, Shifei Ding
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