A novel approach to learning consensus and complementary information for multi-view data clustering
Effective methods are required to be developed that can deal with the multi-faceted nature of
the multi-view data. We design a factorization-based loss function-based method to …
the multi-view data. We design a factorization-based loss function-based method to …
Clustering multi-view data using non-negative matrix factorization and manifold learning for effective understanding: A survey paper
Multi-view data that contains the data represented in many types of features has received
much attention recently. The class of method utilizing non-negative matrix factorization …
much attention recently. The class of method utilizing non-negative matrix factorization …
A novel technique of using coupled matrix and greedy coordinate descent for multi-view data representation
The challenge of clustering multi-view data is to learn all latent features embedded in
multiple views accurately and efficiently. Existing Non-negative matrix factorization based …
multiple views accurately and efficiently. Existing Non-negative matrix factorization based …
Discriminative and orthogonal subspace constraints-based nonnegative matrix factorization
Nonnegative matrix factorization (NMF) is one widely used feature extraction technology in
the tasks of image clustering and image classification. For the former task, various …
the tasks of image clustering and image classification. For the former task, various …
Leveraging maximum entropy and correlation on latent factors for learning representations
Many tasks involve learning representations from matrices, and Non-negative Matrix
Factorization (NMF) has been widely used due to its excellent interpretability. Through …
Factorization (NMF) has been widely used due to its excellent interpretability. Through …
Learning consensus and complementary information for multi-aspect data clustering
One of the most challenging facets of learning multi-aspect data is to effectively capture and
maintain the consensus and complementary information present among multiple views in …
maintain the consensus and complementary information present among multiple views in …
Multi-view image clustering via representations fusion method with semi-nonnegative matrix factorization
G Li, K Han, Z Pan, S Wang, D Song - IEEE Access, 2021 - ieeexplore.ieee.org
Multi-view clustering aims to obtain the perfect clusters with a set of feature sets. Many
methods learn a common agreement among views to achieve this. However, they may fail to …
methods learn a common agreement among views to achieve this. However, they may fail to …
Coupled block diagonal regularization for multi-view subspace clustering
H Chen, W Wang, S Luo - Data Mining and Knowledge Discovery, 2022 - Springer
The object of multi-view subspace clustering is to uncover the latent low-dimensional
structure by segmenting a collection of high-dimensional multi-source data into their …
structure by segmenting a collection of high-dimensional multi-source data into their …
Non-negative Matrix Factorization-Based Multi-aspect Data Clustering
This chapter will discuss the application of Non-negative Matrix Factorization (NMF) in
clustering multi-aspect data. We will begin by providing an overview of the NMF framework …
clustering multi-aspect data. We will begin by providing an overview of the NMF framework …
Ranking preserving nonnegative matrix factorization
Nonnegative matrix factorization (NMF), a wellknown technique to find parts-based
representations of nonnegative data, has been widely studied. In reality, ordinal relations …
representations of nonnegative data, has been widely studied. In reality, ordinal relations …