A5089101157- X-ray Diffraction in Crystallography
- Crystallization and Solubility Studies
- Tensor decomposition and applications
- Face and Expression Recognition
- Quantum Chromodynamics and Particle Interactions
- Computational Physics and Python Applications
- Particle physics theoretical and experimental studies
- Sparse and Compressive Sensing Techniques
- Wound Healing and Treatments
- Neutrino Physics Research
- Electrospun Nanofibers in Biomedical Applications
- Text and Document Classification Technologies
- Hydrogels: synthesis, properties, applications
- Atomic and Subatomic Physics Research
- Retinal Imaging and Analysis
- Traffic Prediction and Management Techniques
- Protein Hydrolysis and Bioactive Peptides
- Insect Utilization and Effects
- Gene expression and cancer classification
- Customer churn and segmentation
- Dark Matter and Cosmic Phenomena
- advanced mathematical theories
- Algal biology and biofuel production
- Advanced Image and Video Retrieval Techniques
- Machine Learning and Data Classification
Anhui Medical University
2023-2024
South China University of Technology
2008-2024
Institute of High Energy Physics
2024
Guizhou Minzu University
2023-2024
Beijing University of Technology
2024
The Segment Anything Model (SAM) has gained popularity as a versatile image segmentation method, thanks to its strong generalization capabilities across various domains. However, when applied optic disc (OD) and cup (OC) tasks, SAM encounters challenges due the complex structures, low contrast, blurred boundaries typical of fundus images, leading suboptimal performance. To overcome these challenges, we introduce novel model, FunduSAM, which incorporates several Adapters into create deep...
Multi-view clustering exploits the complementary information of different views for comprehensive data analysis. Recently, graph learning techniques with low-dimensional embedding have been developed to learn consensus affinity multi-view clustering. However, projecting into space has often resulted in compression information, which is insufficient learning. To address this challenge, paper proposes a Collaborative Embedding Learning via Tensor (CELT) method, learns intra-view graphs each...
Tensor spectral clustering (TSC) is an emerging approach that explores multi-wise similarities to boost learning. However, two key challenges have yet be well addressed in the existing TSC methods: (1) The construction and storage of high-order affinity tensors encode are memory-intensive hampers their applicability, (2) they mostly employ a two-stage integrates multiple different orders learn consensus tensor embedding, thus often leading suboptimal result. To this end, paper proposes...
Unsupervised feature selection is a vital yet challenging topic for effective data learning. Recently, 2-D methods show good performance on image analysis by utilizing the structure information of image. Current usually adopt sparse regularization to spotlight key features. However, such scheme introduces additional hyperparameter needed pruning, limiting applicability unsupervised algorithms. To overcome these challenges, we design filter estimate weight features selection. Theoretical...
Unsupervised feature selection is vital in explanatory learning and remains challenging due to the difficulty of formulating a learnable model. Recently, graph embedding has gained widespread popularity unsupervised learning, which extracts low-dimensional representation based on structure. Nevertheless, such an scheme for will distort original features spatial transformation by extraction. To address this problem, paper proposes collaborative model via jointly using soft-threshold learning....
Graph-based multi-view clustering encodes data into sample affinities to find consensus representation, effectively overcoming heterogeneity across different views. However, traditional affinity measures tend collapse as the feature dimension expands, posing challenges in estimating a unified alignment that reveals both crossview and inner relationships. To tackle this challenge, we propose achieve uniform via representation coregularization. First, are encoded by popular dyadic recent...
Among cyanobacterium, Arthrospira platensis (A. platensis) is a rich source of diverse bioactive compounds due to its high protein, essential amino acid, vitamin, and mineral content. A. one the most abundant sources protein (50–70%). In food industry, being used as an ingredient for development flavor, taste, nutritional composition. Several in vitro vivo studies have revealed potential use prevention treatment various metabolic diseases. Recently, extensive research has focused on...
Traditional clustering methods rely on pairwise affinity to divide samples into different subgroups. However, high-dimensional small-sample (HDLSS) data are affected by the concentration effects, rendering traditional metrics unable accurately describe relationships between samples, leading suboptimal results. This article advances proposition of employing high-order affinities characterize multiple sample as a strategic means circumnavigate effects. We establish nexus order constructing...
The knowledge graph (KG) is a highly needed basis to support the high-fidelity and high-interpretability modeling of various tasks in healthcare artificial intelligence. In this work, we focus on constructing an oncology that will be used downstream cancer research solution development. Modern supervised learning for construction requires large amount manually labeled data, which makes process time-consuming labor-intensive. Although there exists multiple named entity recognition relation...
Tensor spectral clustering (TSC) is a recently proposed approach to robustly group data into underlying clusters. Unlike the traditional (SC), which merely uses pairwise similarities of in an affinity matrix, TSC aims at exploring their multiwise tensor achieve better performance. However, performance highly relies on design similarities, and it remains unclear especially for high-dimension-low-sample-size (HDLSS) data. To this end, article has discriminating (DTSC) HDLSS Specifically, DTSC...
Due to the inherent high-dimensional characteristics of genomic data, traditional single metric/kernel-based clustering methods fail accurately perform data analysis. To address this issue, we propose a multi-kernel with tensor fusion on Grassmann manifold (MKCTM). Specifically, multiple kernel functions are employed map into different spaces and utilize representations capture their high-order relationships. By introducing low-rank constraint, maximize correlation among kernels while...
Ranking potential customers has become an effective tool for company decision makers to design marketing strategies. The task of PAKDD competition 2007 is a cross-selling problem between credit card and home loan, which can also be treated as ranking problem. This article proposes 3-level model, namely Group-Ensemble, handle such kinds problems. In our Bagging, RankBoost Expending Regression Tree are applied solve crucial data mining problems like imbalance, missing value time-variant...
Spectral clustering with graph learning usually performs eigen-decomposition on the adaptive to obtain embedded representation for clustering. In terms of learning, is treated as principal component help improve structure. However, most methods only use a single layer. Therefore, extraction power restricted layer and insufficient explore intrinsic information. To break through this limitation, article proposes stacked network realize spectral (SCnet-AGL). Specifically, allows development...