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Anchor-based sparse subspace incomplete multi-view clustering
In recent decades, multi-view clustering has received a lot of attention. The majority of previous research has assumed that all instances have...
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Incomplete multi-view clustering via attention-based contrast learning
Multi-view clustering (MVC) is an essential and challenging task in machine learning and data mining. In recent years, this field has attracted a lot...
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An adaptive weighted self-representation method for incomplete multi-view clustering
For multi-view data in reality, part of its elements may be missing because of human or machine error. Incomplete multi-view clustering (IMC)...
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Consensus latent incomplete multi-view clustering with low-rank tensor constraint
Traditional multi-view clustering (MVC) assumes that all views are complete and it cannot address a lack of views. In real life, a lack of views...
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Incomplete multi-view clustering based on low-rank representation with adaptive graph regularization
Incomplete multi-view clustering has attracted attention due to its ability to deal with clustering problems with incomplete information. However,...
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Multi-view clustering based on view-attention driven
Multi-view Clustering focuses on discovering coherence information and complementary information about the data among the different views, but often...
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Low-rank tensor learning with projection distance metric for multi-view clustering
Multi-view subspace approaches have been extensively studied for their ability to project data onto a low-dimensional space, which is in favour of...
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Label-noise robust classification with multi-view learning
Label noise is often contained in the training data due to various human factors or measurement errors, which significantly causes a negative effect...
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Multi-view low rank sparse representation method for three-way clustering
During the past years, multi-view clustering algorithms have demonstrated satisfactory clustering results by fusing the multiple views of the...
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A joint optimization framework integrated with biological knowledge for clustering incomplete gene expression data
Clustering algorithms have been successfully applied to identify co-expressed gene groups from gene expression data. Missing values often occur in...
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An Overview of Multi-View Methods for Text Clustering
Text clustering has become an important challenge in text mining and machine learning, which partitions a specific documents’ collection into groups...
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Sequential attention layer-wise fusion network for multi-view classification
Graph convolutional network has shown excellent performance in multi-view classification. Currently, to output a fused node embedding representation...
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Multi-view document clustering based on geometrical similarity measurement
Numerous works implemented multi-view clustering algorithms in document clustering. A challenging problem in document clustering is the similarity...
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A Multi-view Semi-supervised Takagi–Sugeno–Kang Fuzzy System for EEG Emotion Classification
Electroencephalogram (EEG)-based emotion recognition plays an important role in brain-computer interface and mental health monitoring. The large...
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Multi-view data clustering via non-negative matrix factorization with manifold regularization
Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view data shows impressive behavior in machine learning. Usually,...
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Class-structure preserving multi-view correlated discriminant analysis for multiblock data
With the rapid development in data acquisition methods, multiple data sources are now becoming available to explain different views of an object....
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Knowledge-aware progressive clustering for social image
Social image data refer to the annotated image with tags in social media, in which the tags are always labeled by users. Integrating the visual and...
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Diversity-promoting multi-view graph learning for semi-supervised classification
In this paper, we focus on how to boost the semi-supervised classification performance by exploring the multi-view graph learning. The key of...
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Fuzzy K-means clustering with reconstructed information
Clustering techniques play a pivotal role in unveiling the inherent structure of unlabeled data. When dealing with overlap** clusters, traditional...
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Spectral Clustering on Multi-aspect Data
The spectral clustering techniques, based on the well-established spectral graph theory, are among the modern and effective clustering approaches....