Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering
Multi-view data clustering based on Non-negative Matrix Factorization (NMF) has been
commonly used for pattern recognition by grouping multi-view high-dimensional data by …
commonly used for pattern recognition by grouping multi-view high-dimensional data by …
Machine learning on cloud with blockchain: a secure, verifiable and fair approach to outsource the linear regression
Linear Regression (LR) is a classical machine learning algorithm which has many
applications in the cyber physical social systems (CPSS) to shape and simplify the way we …
applications in the cyber physical social systems (CPSS) to shape and simplify the way we …
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 …
Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization
Social media platforms like Twitter have become an easy portal for billions of people to
connect and exchange their thoughts. Unfortunately, people commonly use these platforms …
connect and exchange their thoughts. Unfortunately, people commonly use these platforms …
Consistency–exclusivity guided unsupervised multi-view feature selection
S Zhou, P Song - Neurocomputing, 2024 - Elsevier
Unsupervised multi-view feature selection (UMFS) is an effective dimension reduction for
multi-view data. It aims to obtain the important feature subset from multi-view data, which can …
multi-view data. It aims to obtain the important feature subset from multi-view data, which can …
Adaptive multi-view multiple-means clustering via subspace reconstruction
W Liu, L Liu, Y Zhang, H Wang, L Feng - Engineering Applications of …, 2022 - Elsevier
Clustering is a notable research topic, but it is still challenging when facing massive multi-
view data from different ways or multiple feature extractors. The crucial problem is how to …
view data from different ways or multiple feature extractors. The crucial problem is how to …
Deep hierarchical non-negative matrix factorization for clustering short text
WA Mohotti, R Nayak - … , ICONIP 2020, Bangkok, Thailand, November 23 …, 2020 - Springer
This paper proposes a deep hierarchical Non-negative Matrix Factorization (NMF) method
with Skip-Gram with Negative sampling (SGNS) to learn semantic relationships in short text …
with Skip-Gram with Negative sampling (SGNS) to learn semantic relationships in short text …
Robust Multi-view Clustering via Graph-oriented High-order Correlations Learning
W Liu, J Zhu, H Wang, Y Zhang - IEEE Transactions on Network …, 2024 - ieeexplore.ieee.org
Multi-view clustering aims to partition data into corresponding clusters by leveraging
features from various views to reveal the underlying structure of the data fully. However …
features from various views to reveal the underlying structure of the data fully. However …
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 …
Column-wise element selection for computationally efficient nonnegative coupled matrix tensor factorization
Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of
multiple data sources and helps discover meaningful information. Nonnegative CMTF (N …
multiple data sources and helps discover meaningful information. Nonnegative CMTF (N …