Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints
Multi-view clustering (MVC) has received extensive attention due to its efficient processing of
high-dimensional data. Most of the existing multi-view clustering methods are based on non …
high-dimensional data. Most of the existing multi-view clustering methods are based on non …
Attention-driven graph clustering network
The combination of the traditional convolutional network (ie, an auto-encoder) and the graph
convolutional network has attracted much attention in clustering, in which the auto-encoder …
convolutional network has attracted much attention in clustering, in which the auto-encoder …
Semi-supervised constrained clustering: An in-depth overview, ranked taxonomy and future research directions
G González-Almagro, D Peralta, E De Poorter… - ar** discrete sets of instances with similar characteristics. Constrained …
Self-guided partial graph propagation for incomplete multiview clustering
In this work, we study a more realistic challenging scenario in multiview clustering (MVC),
referred to as incomplete MVC (IMVC) where some instances in certain views are missing …
referred to as incomplete MVC (IMVC) where some instances in certain views are missing …
Dual semi-supervised convex nonnegative matrix factorization for data representation
Semi-supervised nonnegative matrix factorization (NMF) has received considerable
attention in machine learning and data mining. A new semi-supervised NMF method, called …
attention in machine learning and data mining. A new semi-supervised NMF method, called …
Multiview clustering via hypergraph induced semi-supervised symmetric nonnegative matrix factorization
Nonnegative matrix factorization (NMF) based multiview technique has been commonly
used in multiview data clustering tasks. However, previous NMF based multiview clustering …
used in multiview data clustering tasks. However, previous NMF based multiview clustering …
Multi-label classification with high-rank and high-order label correlations
Exploiting label correlations is important to multi-label classification. Previous methods
capture the high-order label correlations mainly by transforming the label matrix to a latent …
capture the high-order label correlations mainly by transforming the label matrix to a latent …
Mt-ncov-net: a multitask deep-learning framework for efficient diagnosis of covid-19 using tomography scans
W Ding, M Abdel-Basset, H Hawash… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The localization and segmentation of the novel coronavirus disease of 2019 (COVID-19)
lesions from computerized tomography (CT) scans are of great significance for develo** …
lesions from computerized tomography (CT) scans are of great significance for develo** …
Maximum entropy subspace clustering network
Deep subspace clustering networks have attracted much attention in subspace clustering, in
which an auto-encoder non-linearly maps the input data into a latent space, and a fully …
which an auto-encoder non-linearly maps the input data into a latent space, and a fully …
Subspace learning for facial expression recognition: an overview and a new perspective
For image recognition, an extensive number of subspace-learning methods have been
proposed to overcome the high-dimensionality problem of the features being used. In this …
proposed to overcome the high-dimensionality problem of the features being used. In this …