An observational analysis of the trope “A p-value of< 0.05 was considered statistically significant” and other cut-and-paste statistical methods
Appropriate descriptions of statistical methods are essential for evaluating research quality
and reproducibility. Despite continued efforts to improve reporting in publications …
and reproducibility. Despite continued efforts to improve reporting in publications …
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 …
Learning inter-and intra-manifolds for matrix factorization-based multi-aspect data clustering
Clustering on the data with multiple aspects, such as multi-view or multi-type relational data,
has become popular in recent years due to their wide applicability. The approach using …
has become popular in recent years due to their wide applicability. The approach using …
[HTML][HTML] Multi-view feature engineering for day-to-day joint clustering of multiple traffic datasets
A common task in traffic data analysis and management is categorizing different days based
on similarities in their network-wide traffic states. Given the multifaceted nature of traffic, it is …
on similarities in their network-wide traffic states. Given the multifaceted nature of traffic, it is …
Diverse embeddings learning for multi-view clustering
Y Li, H Liao - Pattern Analysis and Applications, 2025 - Springer
Multi-view clustering, which improves clustering performance by exploring complementarity
and consistency among multiple distinct feature sets, is attracting more and more …
and consistency among multiple distinct feature sets, is attracting more and more …
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 …
Multi-type relational data clustering for community detection by exploiting content and structure information in social networks
Social Networks popularity has facilitated the providers with an opportunity to target specific
user groups for various applications such as viral marketing and customized programs …
user groups for various applications such as viral marketing and customized programs …
Multi-aspect Data Learning: Overview, Challenges and Approaches
Multi-aspect data, which represents information from multiple perspectives, is becoming
increasingly common and important. This is because such data has the ability to incorporate …
increasingly common and important. This is because such data has the ability to incorporate …
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 …