Learning Robust Discriminant Subspace Based on Joint L₂,ₚ- and L₂,ₛ-Norm Distance Metrics
Recently, there are many works on discriminant analysis, which promote the robustness of
models against outliers by using L 1-or L 2, 1-norm as the distance metric. However, both of …
models against outliers by using L 1-or L 2, 1-norm as the distance metric. However, both of …
Self-supervised graph convolutional network for multi-view clustering
Despite the promising preliminary results, existing graph convolutional network (GCN)
based multi-view learning methods directly use the graph structure as view descriptor, which …
based multi-view learning methods directly use the graph structure as view descriptor, which …
Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation
Multi-view clustering has become an active topic in artificial intelligence. Yet, similar
investigation for graph-structured data clustering has been absent so far. To fill this gap, we …
investigation for graph-structured data clustering has been absent so far. To fill this gap, we …
Multi-view projected clustering with graph learning
Graph based multi-view learning is well known due to its effectiveness and good clustering
performance. However, most existing methods directly construct graph from original high …
performance. However, most existing methods directly construct graph from original high …
Generalized centered 2-D principal component analysis
Most existing robust principal component analysis (PCA) and 2-D PCA (2DPCA) methods
involving the-norm can mitigate the sensitivity to outliers in the domains of image analysis …
involving the-norm can mitigate the sensitivity to outliers in the domains of image analysis …
Robust GEPSVM classifier: An efficient iterative optimization framework
The proximal support vector machine via generalized eigenvalues (GEPSVM) is a well-
known pattern classification method. GEPSVM, however, is prone to outliers due to its use of …
known pattern classification method. GEPSVM, however, is prone to outliers due to its use of …
High-Accuracy Classification of Attention Deficit Hyperactivity Disorder With l2,1-Norm Linear Discriminant Analysis and Binary Hypothesis Testing
Y Tang, X Li, Y Chen, Y Zhong, A Jiang, C Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral
disease in school-age children. Its neurobiological diagnosis (or classification) is meaningful …
disease in school-age children. Its neurobiological diagnosis (or classification) is meaningful …
Euler common spatial patterns for EEG classification
J Sun, M Wei, N Luo, Z Li, H Wang - Medical & Biological Engineering & …, 2022 - Springer
The technique of common spatial patterns (CSP) is a widely used method in the field of
feature extraction of electroencephalogram (EEG) signals. Motivated by the fact that a cosine …
feature extraction of electroencephalogram (EEG) signals. Motivated by the fact that a cosine …
A novel online sequential extreme learning machine with L2,1-norm regularization for prediction problems
Preeti, R Bala, A Dagar, RP Singh - Applied Intelligence, 2021 - Springer
In today's world, data is produced at a very high speed and used in a large number of
prediction problems. Therefore, the sequential nature of learning algorithms is in demand for …
prediction problems. Therefore, the sequential nature of learning algorithms is in demand for …
Improvement accuracy in deep learning: An increasing neurons distance approach with the penalty term of loss function
The increasing use of neural networks for solving complex tasks has emphasized the need
to optimize their performance. In recent years, the development of neural networks has …
to optimize their performance. In recent years, the development of neural networks has …