Various dimension reduction techniques for high dimensional data analysis: a review
In the era of healthcare, and its related research fields, the dimensionality problem of high
dimensional data is a massive challenge as it contains a huge number of variables forming …
dimensional data is a massive challenge as it contains a huge number of variables forming …
A novel approach to large-scale dynamically weighted directed network representation
A d ynamically w eighted d irected n etwork (DWDN) is frequently encountered in various big
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
GMC: Graph-based multi-view clustering
Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
However, most existing methods do not give sufficient consideration to weights of different …
However, most existing methods do not give sufficient consideration to weights of different …
A latent factor analysis-based approach to online sparse streaming feature selection
Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …
decades. Current approaches commonly assume that the feature space of fixed data …
Multiview consensus graph clustering
A graph is usually formed to reveal the relationship between data points and graph structure
is encoded by the affinity matrix. Most graph-based multiview clustering methods use …
is encoded by the affinity matrix. Most graph-based multiview clustering methods use …
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 …
Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors
Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns
hidden in such dynamic data for predicting missing ones with high accuracy. However …
hidden in such dynamic data for predicting missing ones with high accuracy. However …
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 …
Advancing non-negative latent factorization of tensors with diversified regularization schemes
Dynamic relationships are frequently encountered in big data and services computing-
related applications, like dynamic data of user-side QoS in Web services. They are modeled …
related applications, like dynamic data of user-side QoS in Web services. They are modeled …
Projected cross-view learning for unbalanced incomplete multi-view clustering
Incomplete multi-view clustering (IMVC) aims to partition samples into different groups for
datasets with missing samples. The primary goal of IMVC is to effectively address the …
datasets with missing samples. The primary goal of IMVC is to effectively address the …