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Auto-weighted multi-view learning for image clustering and semi-supervised classification
Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance …
oriented methods have been widely investigated and achieve promising performance …
Inductive graph unlearning
As a way to implement the" right to be forgotten" in machine learning, machine unlearning
aims to completely remove the contributions and information of the samples to be deleted …
aims to completely remove the contributions and information of the samples to be deleted …
Sample-level cross-view similarity learning for incomplete multi-view clustering
Incomplete multi-view clustering has attracted much attention due to its ability to handle
partial multi-view data. Recently, similarity-based methods have been developed to explore …
partial multi-view data. Recently, similarity-based methods have been developed to explore …
Generalized uncorrelated regression with adaptive graph for unsupervised feature selection
Unsupervised feature selection always occupies a key position as a preprocessing in the
tasks of classification or clustering due to the existence of extra essential features within high …
tasks of classification or clustering due to the existence of extra essential features within high …
Multi-view clustering via nonnegative and orthogonal graph reconstruction
The goal of multi-view clustering is to partition samples into different subsets according to
their diverse features. Previous multi-view clustering methods mainly exist two forms: multi …
their diverse features. Previous multi-view clustering methods mainly exist two forms: multi …
Multilabel feature selection via shared latent sublabel structure and simultaneous orthogonal basis clustering
Multilabel feature selection solves the dimension distress of high-dimensional multilabel
data by selecting the optimal subset of features. Noisy and incomplete labels of raw …
data by selecting the optimal subset of features. Noisy and incomplete labels of raw …
Graph embedding orthogonal decomposition: A synchronous feature selection technique based on collaborative particle swarm optimization
In unsupervised feature selection, the clustering label matrix has the ability to distinguish
between projection clusters. However, the latent geometric structure of the clustering labels …
between projection clusters. However, the latent geometric structure of the clustering labels …
Multiview clustering by consensus spectral rotation fusion
Multiview clustering (MVC) aims to partition data into different groups by taking full
advantage of the complementary information from multiple views. Most existing MVC …
advantage of the complementary information from multiple views. Most existing MVC …
Multi-view subspace clustering via adaptive graph learning and late fusion alignment
Multi-view subspace clustering has attracted great attention due to its ability to explore data
structure by utilizing complementary information from different views. Most of existing …
structure by utilizing complementary information from different views. Most of existing …
Spectral embedding fusion for incomplete multiview clustering
Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete
multiview data by partitioning data samples into clusters. Several graph-based methods …
multiview data by partitioning data samples into clusters. Several graph-based methods …