Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis
Feature selection, as a dimension reduction technique in data mining and pattern
recognition, aims to select the most discriminative features and improve the learning …
recognition, aims to select the most discriminative features and improve the learning …
T-distributed Stochastic Neighbor Network for unsupervised representation learning
Unsupervised representation learning (URL) is still lack of a reasonable operator (eg
convolution kernel) for exploring meaningful structural information from generic data …
convolution kernel) for exploring meaningful structural information from generic data …
SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation
H Luo, C **ang, L Zeng, S Li, X Mei, L **ong, Y Liu… - Scientific Reports, 2024 - nature.com
Heart failure (HF) is a significant global public health concern with a high readmission rate,
posing a serious threat to the health of the elderly population. While several studies have …
posing a serious threat to the health of the elderly population. While several studies have …
Sparse discriminant manifold projections for automatic depression recognition
L Zhang, J Zhong, Q Zhao, S Qiao, Y Wu, B Hu, S Ma… - Neurocomputing, 2025 - Elsevier
In recent years, depression has become an increasingly serious problem globally. Previous
research have shown that EEG-based depression recognition is a promising technique to …
research have shown that EEG-based depression recognition is a promising technique to …
Local sparse discriminative feature selection
Feature selection has been widely used in machine learning for a long time. In this paper,
we propose a supervised local sparse discriminative feature selection method named …
we propose a supervised local sparse discriminative feature selection method named …
Adaptive Graph Convolutional Network for Unsupervised Generalizable Tabular Representation Learning
A challenging open problem in deep learning is the representation of tabular data. Unlike
the popular domains such as image and text understanding, where the deep convolutional …
the popular domains such as image and text understanding, where the deep convolutional …
Joint Structured Bipartite Graph and Row-Sparse Projection for Large-Scale Feature Selection
Feature selection plays an important role in data analysis, yet traditional graph-based
methods often produce suboptimal results. These methods typically follow a two-stage …
methods often produce suboptimal results. These methods typically follow a two-stage …
Capped norm based discriminant robust regression learning
Exploring underlying correlation structures among data, improving robustness to noise,
obtaining discriminant projections, integrating local information of data, and selecting …
obtaining discriminant projections, integrating local information of data, and selecting …
Adaptive Neighbors Graph Learning for Large-Scale Data Clustering using Vector Quantization and Self-Regularization
In traditional adaptive neighbors graph learning (ANGL)-based clustering, the time
complexity is more than O (n 2), where n is the number of data points, which is not scalable …
complexity is more than O (n 2), where n is the number of data points, which is not scalable …
Linear centroid encoder for supervised principal component analysis
We propose a new supervised dimensionality reduction technique called Supervised Linear
Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder …
Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder …