A5102760584- High-Energy Particle Collisions Research
- Particle physics theoretical and experimental studies
- Quantum Chromodynamics and Particle Interactions
- Face and Expression Recognition
- Advanced Clustering Algorithms Research
- Text and Document Classification Technologies
- Emotion and Mood Recognition
- Gaze Tracking and Assistive Technology
- EEG and Brain-Computer Interfaces
- Video Surveillance and Tracking Methods
- Soil Mechanics and Vehicle Dynamics
- Advanced Graph Neural Networks
- Advanced Computing and Algorithms
- Advanced Image and Video Retrieval Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Phonetics and Phonology Research
- Remote-Sensing Image Classification
- Domain Adaptation and Few-Shot Learning
- Data Management and Algorithms
- Linguistic Variation and Morphology
- Tree Root and Stability Studies
- Machine Learning and ELM
- Transportation Safety and Impact Analysis
- Alzheimer's disease research and treatments
National University of Defense Technology
2022-2025
Nanjing Agricultural University
2023
Ministry of Agriculture and Rural Affairs
2023
Northwest Institute of Mechanical and Electrical Engineering
2022-2023
Northwest A&F University
2021-2023
Southeast University
2018-2021
Shandong University
2021
A. Alikhanyan National Laboratory
2020
University of California, Los Angeles
2020
Shenyang 242 Hospital
2019
Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve performance. Recently, many anchor-based variants are proposed reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most them adopt fixed anchor points separating from subsequential construction, which may adversely affect In addition, post-processing is required generate discrete labels with additional time consumption. To address...
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe following drawback, which limits performance further improvement. The existing models mainly focus on consistency same samples different views while ignoring circumstance similar but cross-view scenarios. To solve this problem, propose a novel Dual calibration network for Multi-View Clustering (DealMVC). Specifically,...
Anchor-based multi-view graph clustering has recently gained popularity as an effective approach for data with multiple views. However, existing methods have limitations in terms of handling inconsistent information and noise across views, resulting unreliable consensus representation. Additionally, post-processing is needed to obtain final results after anchor construction, which negatively affects performance. In this paper, we propose a Robust Consistent Anchor Graph Learning method...
Emotion recognition from electroencephalograph (EEG) signals has long been essential for affective computing. In this article, we evaluate EEG emotion by converting multiple channels into images such that richer spatial information can be considered and the question of EEG-based converted image recognition. To end, propose a novel method to generate continuous discrete introducing offset variables following Gaussian distribution each channel alleviate biased electrode coordinates during...
The individual differences and the dynamic uncertain relationships among different electroencephalogram (EEG) regions are essential factors that limit EEG emotion recognition. To address these issues, in this article, we propose a variational instance-adaptive graph method (V-IAG) simultaneously captures dependencies electrodes estimates underlying information. Specifically, employ two branches, i.e., branch branch, to construct graph. Inspired by attention mechanism, generates based on...
Multi-view clustering (MVC) methods aim to exploit consistent and complementary information among each view achieve encouraging performance improvement than single-view counterparts. In practical applications, it is common obtain instances with partially available information, raising researches of incomplete multi-view (IMC) issues. Recently, several fast IMC have been proposed process the large-scale partial data. Though considerable acceleration, these seek view-shared anchors ignore...
Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to reduce the complexity cost. However, due sampling operation being performed on each individual view independently and not considering distribution of samples all views, produced anchors are usually slightly distinguishable, failing characterize whole data. Moreover, it necessary fuse multiple separated graphs into one, which leads final performance heavily subject fusion algorithm adopted. What worse,...
Incomplete multi-view clustering has attracted much attention due to its ability handle partial data. Recently, similarity-based methods have been developed explore the complete relationship among incomplete Although widely applied scenarios, most of existing approaches are still faced with two limitations. Firstly, fusing similarities constructed individually on each view fails yield a unified similarity. Moreover, similarity generation may lead anomalous values column sum constraints,...
Multi-view graph clustering (MVGC) derives encouraging grouping results by seamlessly integrating abundant information inside heterogeneous data, and has captured surging focus recently. Nevertheless, the majority of current MVGC works involve at least one hyper-parameter, which not only requires additional efforts for tuning, but also leads to a complicated solving procedure, largely harming flexibility scalability corresponding algorithms. To this end, in article we are devoted getting rid...
The difficulty of emotion recognition in the wild (EmotiW) is how to train a robust model deal with diverse scenarios and anomalies. Audio-video Sub-challenge EmotiW contains audio-video short clips several emotional labels task distinguish which label video belongs to. For better videos, we propose multiple spatio-temporal feature fusion (MSFF) framework, can more accurately depict information spatial temporal dimensions by two mutually complementary sources, including facial image audio....
To tackle the individual differences and characterize dynamic relationships among different EEG regions for emotion recognition, in this paper, we propose a novel instance-adaptive graph method (IAG), which employs more flexible way to construct graphic connections so as present representations determined by input instances. fit pattern, employ an additional branch intrinsic between channels. give precise representation, design multi-level multi-graph convolutional operation coarsening....
Objectives: This diet-controlled study was designed to examine effects of konjac glucomannan (KGM) supplement on the bowel habits and colonic ecology in 7 constipated subjects. In addition, mechanisms by which KGM modulated habit were explored.Methods: Seven subjects who passed movement less than once a day participated this linear that consisted 21-d placebo period, 7-d adaptation KGM-supplemented (1.5 g, tid) period. The large response fecal characteristics recorded daily. Stools collected...
Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of methods. Although widely applied in large-scale applications, existing approaches do not pay sufficient attention establishing correct correspondences between sets across views. To be specific, graphs obtained from different views are aligned column-wisely. Such an \textbf{A}nchor-\textbf{U}naligned \textbf{P}roblem (AUP) would cause inaccurate fusion...
Multiview clustering (MVC) seamlessly combines homogeneous information and allocates data samples into different communities, which has shown significant effectiveness for unsupervised tasks in recent years. However, some views of may be incomplete due to unfinished collection or storage failure reality, refers the so-called multiview (IMVC). Despite many IMVC pioneer frameworks have been introduced, majority their approaches are limited by cubic time complexity quadratic space heavily...
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability capture complementary structural information across multiple views. Intuitively, a high-quality anchor plays an essential role in success of AMVGC. However, existing AMVGC methods only consider single-structure information, i.e., local or global structure, which provides insufficient for learning task. To be specific, over-scattered structure leads learned anchors...
Anchor graph has been recently proposed to accelerate multi-view clustering and widely applied in various large-scale applications. Different from capturing full instance relationships, these methods choose small portion anchors among each view, construct single-view anchor graphs combine them into the unified graph. Despite its efficiency, we observe that: (i) Existing mechanism adopts a separable two-step procedure-anchor construction individual fusion, which may degrade performance....
Existing Multiple Kernel Clustering (MKC) algorithms commonly utilize the Nyström method to handle large-scale datasets. However, most of them employ uniform sampling for kernel matrix approximation, hence failing accurately capture underlying data structure, leading large approximation errors. Additionally, they often use same landmark points all approximations, reducing diversity. Moreover, in scenarios where approximate matrices emerge over time, these methods require storing historical...
Wild chrysanthemum has a high medicinal value. Its mechanized harvest can improve harvesting efficiency, reduce labor costs and planting benefits, which is an important way to promote artificial planting. However, one of the difficulties in large diameter hardness stem, leading cutting resistance power consumption. In order consumption, bionic blade designed this paper by employing bionics principle contour cricket’s upper jaw incisor lobe instead sharp triangular teeth standard harvester...
The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples usually partially available due to data corruption or sensor malfunction, which raises research incomplete (IMVC). Although several anchor-based IMVC methods have been proposed process large-scale data, they still suffer from following drawbacks: i) Most approaches neglect inter-view discrepancy and enforce cross-view representation be consistent, would corrupt...
Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore interconnectedness of disease occurrence through multi-omics studies validate it large-scale electronic records. In response, research examined data from 160 sub-healthy individuals at high altitude then deep learning model called Omicsformer is developed for detailed analysis...
Due to a lack of an accurate model in finite element simulation mechanized harvesting wild chrysanthemum, the stem chrysanthemum period is taken as research object. ANSYS Workbench 19.0 software and LS-DYNA (LS-PrePOST-4.3-X64) are used calibrate cutting. The diameter distribution at cutting height obtained. maximum shear forces different diameters (7 mm, 8 9 10 11 mm) within range determined 120.0 N, 159.2 213.8 300.0 378.2 respectively, by using biomechanical testing machine custom-made...
Multi-view subspace clustering (MVSC) is a popular area of research that concentrates on partitioning data points from multiple views. It has gained wide attention in recent years due to the ability handle complex with diverse features across different However, success MVSC largely relies quality learned similarity matrix, and existing methods normally adopt separate two-step procedures optimization symmetrization, which could not guarantee symmetry adaptive locality matrix. To alleviate...
Multiple kernel clustering (MKC) enhances performance by deriving a consensus partition or graph from predefined set of kernels. Despite many advanced MKC methods proposed in recent years, the prevalent approaches involve incorporating all kernels default to capture diverse information within data. However, learning may not be better than one few kernels, particularly since some exhibit higher proportion noise semantic content. Additionally, existing methods, whether based on early-fusion...