780 citations · 1.6k across the 54 of their papers we have counts for
6 papers · 1 filter
Study of General Robust Subband Adaptive Filtering
Yi Yu, Hongsen He, Rodrigo C. de Lamare +1
In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model wi…
Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding
Yuanhao Li, Badong Chen, Yuxi Shi +2
Recent studies have utilized sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's intentions and mental states, se…
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation
Yuehai Chen, Jing Yang, Badong Chen +1
In real-world crowd counting applications, the crowd densities in an image vary greatly. When facing density variation, humans tend to locate and count the targets in low-density r…
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
Kaizhong Zheng, Shujian Yu, Baojuan Li +2
Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machi…
Multi-view Information Bottleneck Without Variational Approximation
Qi Zhang, Shujian Yu, Jingmin Xin +1
By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we ext…
Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments
Y. Yu, L. Lu, Y. Zakharov +2
This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algo…