research
∙
09/30/2022
Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction
Interpreting critical variables involved in complex biological processes...
share
research
∙
08/25/2022
PRIME: Uncovering Circadian Oscillation Patterns and Associations with AD in Untimed Genome-wide Gene Expression across Multiple Brain Regions
The disruption of circadian rhythm is a cardinal symptom for Alzheimer's...
research
∙
08/25/2022
Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets
Alzheimer's disease (AD), as a progressive brain disease, affects cognit...
research
∙
06/20/2022
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection
The existing state-of-the-art (SOTA) video salient object detection (VSO...
research
∙
04/22/2022
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Nonnegative matrix factorization (NMF) has been widely studied in recent...
research
∙
11/03/2020
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
Subspace clustering methods have been widely studied recently. When the ...
research
∙
08/31/2020
Structured Graph Learning for Clustering and Semi-supervised Classification
Graphs have become increasingly popular in modeling structures and inter...
research
∙
08/07/2020
Full Reference Screen Content Image Quality Assessment by Fusing Multi-level Structure Similarity
The screen content images (SCIs) usually comprise various content types ...
research
∙
08/07/2020
Depth Quality Aware Salient Object Detection
The existing fusion based RGB-D salient object detection methods usually...
research
∙
08/07/2020
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
The current main stream methods formulate their video saliency mainly fr...
research
∙
08/07/2020
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
Previous video salient object detection (VSOD) approaches have mainly fo...
research
∙
05/19/2020
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
In this paper, we propose a new Semi-Nonnegative Matrix Factorization me...
research
∙
12/03/2019
Structure Learning with Similarity Preserving
Leveraging on the underlying low-dimensional structure of data, low-rank...
research
∙
07/13/2019
A stabilized total-Lagrangian SPH method for large deformation and failure in geomaterials
Conventional smoothed particle hydrodynamics based on Eulerian kernels (...
research
∙
07/09/2019
Nonnegative Matrix Factorization with Local Similarity Learning
Existing nonnegative matrix factorization methods focus on learning glob...
research
∙
04/16/2019
RES-PCA: A Scalable Approach to Recovering Low-rank Matrices
Robust principal component analysis (RPCA) has drawn significant attenti...
research
∙
04/16/2019
Discriminative Regression Machine: A Classifier for High-Dimensional Data or Imbalanced Data
We introduce a discriminative regression approach to supervised classifi...
research
∙
04/04/2019
Numerical Simulation of Metal Machining Process with Eulerian and Total Lagrangian SPH
This paper presents numerical simulations of metal machining processes w...
research
∙
01/24/2019
A Total Lagrangian SPH Method for Modelling Damage and Failure in Solids
An algorithm is proposed to model crack initiation and propagation withi...
research
∙
11/12/2017
Unified Spectral Clustering with Optimal Graph
Spectral clustering has found extensive use in many areas. Most traditio...
research
∙
05/01/2017
Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Many similarity-based clustering methods work in two separate steps incl...
research
∙
09/27/2016
A Fast Factorization-based Approach to Robust PCA
Robust principal component analysis (RPCA) has been widely used for reco...
research
∙
09/27/2016
Top-N Recommendation on Graphs
Recommender systems play an increasingly important role in online applic...
research
∙
01/19/2016
Top-N Recommender System via Matrix Completion
Top-N recommender systems have been investigated widely both in industry...
research
∙
11/17/2015
Robust PCA via Nonconvex Rank Approximation
Numerous applications in data mining and machine learning require recove...
research
∙
10/30/2015
Robust Subspace Clustering via Tighter Rank Approximation
Matrix rank minimization problem is in general NP-hard. The nuclear norm...
research
∙
08/18/2015
Robust Subspace Clustering via Smoothed Rank Approximation
Matrix rank minimizing subject to affine constraints arises in many appl...
research
∙
07/03/2015