Adaptive reverse graph learning for robust subspace learning
Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …
original data into a low-dimensional subspace, as well as preserving the similarity among …
Unsupervised feature selection through combining graph learning and ℓ2, 0-norm constraint
Graph-based unsupervised feature selection algorithms have been shown to be promising
for handling unlabeled and high-dimensional data. Whereas, the vast majority of those …
for handling unlabeled and high-dimensional data. Whereas, the vast majority of those …
Deepintent: Deep icon-behavior learning for detecting intention-behavior discrepancy in mobile apps
Mobile apps have been an indispensable part in our daily life. However, there exist many
potentially harmful apps that may exploit users' privacy data, eg, collecting the user's …
potentially harmful apps that may exploit users' privacy data, eg, collecting the user's …
Graph entropy guided node embedding dimension selection for graph neural networks
Graph representation learning has achieved great success in many areas, including e-
commerce, chemistry, biology, etc. However, the fundamental problem of choosing the …
commerce, chemistry, biology, etc. However, the fundamental problem of choosing the …
Collaborative and Discriminative Subspace Learning for unsupervised multi-view feature selection
JS Wu, Y Li, JX Gong, W Min - Engineering Applications of Artificial …, 2024 - Elsevier
By effectively exploiting the consistent information of multi-view data, multi-view feature
selection seeks to select crucial features from multiple heterogeneous data views to improve …
selection seeks to select crucial features from multiple heterogeneous data views to improve …
Unsupervised subspace learning with flexible neighboring
Graph-based subspace learning has been widely used in various applications as the rapid
growth of data dimension, while the graph is constructed by affinity matrix of input data …
growth of data dimension, while the graph is constructed by affinity matrix of input data …
Neural auto-designer for enhanced quantum kernels
Quantum kernels hold great promise for offering computational advantages over classical
learners, with the effectiveness of these kernels closely tied to the design of the quantum …
learners, with the effectiveness of these kernels closely tied to the design of the quantum …
Robust Structure-aware Semi-supervised Learning
X Chen - 2022 IEEE International Conference on Data Mining …, 2022 - ieeexplore.ieee.org
We present a novel unified framework robust structure-aware semi-supervised learning
called Unified RSSL (URSSL) which is robust to both outliers and noisy labels where the …
called Unified RSSL (URSSL) which is robust to both outliers and noisy labels where the …
Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing
X Chen - ACM Transactions on Intelligent Systems and …, 2024 - dl.acm.org
Graph-based semi-supervised learning plays an important role in large scale image
classification tasks. However, the problem becomes very challenging in the presence of …
classification tasks. However, the problem becomes very challenging in the presence of …
Improving node embedding by a compact neighborhood representation
Graph Embedding, a learning paradigm that represents graph vertices, edges, and other
semantic information about a graph into low-dimensional vectors, has found wide …
semantic information about a graph into low-dimensional vectors, has found wide …