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View article: Transcriptomic insights into the effects of Hepatospora eriocheir (Microsporidia) infection in Eriocheir sinensis
Transcriptomic insights into the effects of Hepatospora eriocheir (Microsporidia) infection in Eriocheir sinensis Open
The Chinese mitten crab (Eriocheir sinensis), a key species in China’s freshwater aquaculture, faces growing threats from Hepatopancreatic Necrosis Syndrome caused by the microsporidian parasite Hepatospora eriocheir. Deciphering the molec…
View article: Improving Nutrition and Sensory Goals: Utilizing Explainable Machine Learning and Multi‐Objective Optimization to Optimize Quality of <i>Osmanthus fragrans</i> Extract
Improving Nutrition and Sensory Goals: Utilizing Explainable Machine Learning and Multi‐Objective Optimization to Optimize Quality of <i>Osmanthus fragrans</i> Extract Open
Osmanthus fragrans extract (OFE) has significant potential for application in the beverage and cosmetic industries. However, the conventional extraction processes of OFE are affected by multiple factors, making it challenging to identify p…
View article: Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing
Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing Open
Knowledge Tracing (KT) involves monitoring the changes in a student's knowledge over time by analyzing their past responses, with the goal of predicting future performance. However, most existing methods primarily focus on feature enhancem…
View article: Language Pre-training Guided Masking Representation Learning for Time Series Classification
Language Pre-training Guided Masking Representation Learning for Time Series Classification Open
The representation learning of time series has a wide range of downstream tasks and applications in many practical scenarios. However, due to the complexity, spatiotemporality, and continuity of sequential stream data, compared with the re…
View article: Riemannian Optimization on Relaxed Indicator Matrix Manifold
Riemannian Optimization on Relaxed Indicator Matrix Manifold Open
The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this relaxation forms a manifold, which we call the Relaxed Indic…
View article: Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering Open
Min cut is an important graph partitioning method. However, current solutions to the min cut problem suffer from slow speeds, difficulty in solving, and often converge to simple solutions. To address these issues, we relax the min cut prob…
View article: A Band Selection Approach Based on a Mass‐Based Metric and Shared Nearest‐Neighbours for Hyperspectral Images
A Band Selection Approach Based on a Mass‐Based Metric and Shared Nearest‐Neighbours for Hyperspectral Images Open
Band selection in hyperspectral imaging is a burgeoning research area whose aim is to select a small number of bands in order to reduce data redundancy and noise bands. The existing ranking‐based methods face two challenges: (1) The densit…
View article: A Greedy Strategy for Graph Cut
A Greedy Strategy for Graph Cut Open
We propose a Greedy strategy to solve the problem of Graph Cut, called GGC. It starts from the state where each data sample is regarded as a cluster and dynamically merges the two clusters which reduces the value of the global objective fu…
View article: Clustering Based on Density Propagation and Subcluster Merging
Clustering Based on Density Propagation and Subcluster Merging Open
We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which nec…
View article: Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method
Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method Open
In the graph-based semi-supervised learning, the Green-function method is a classical method that works by computing the Green's function in the graph space. However, when applied to large graphs, especially those sparse ones, this method …
View article: Genome-Wide Identification and Analysis of Gene Family of Carbohydrate-Binding Modules in Ustilago crameri
Genome-Wide Identification and Analysis of Gene Family of Carbohydrate-Binding Modules in Ustilago crameri Open
Ustilago crameri is a pathogenic basidiomycete fungus that causes foxtail millet kernel smut (FMKS), a devastating grain disease in most foxtail millet growing regions of the world. Carbohydrate-Binding Modules (CBMs) are one of the import…
View article: Self-adjusted graph based semi-supervised embedded feature selection
Self-adjusted graph based semi-supervised embedded feature selection Open
Graph-based semi-supervised feature selection has aroused continuous attention in processing high-dimensional data with most unlabeled and fewer data samples. Many graph-based models perform on a pre-defined graph, which is separated from …
View article: A Margin-Maximizing Fine-Grained Ensemble Method
A Margin-Maximizing Fine-Grained Ensemble Method Open
Ensemble learning has achieved remarkable success in machine learning, but its reliance on numerous base learners limits its application in resource-constrained environments. This paper introduces an innovative "Margin-Maximizing Fine-Grai…
View article: A comprehensive survey of fast graph clustering
A comprehensive survey of fast graph clustering Open
Graph clustering methods are popular due to their ability to discover clusters with arbitrary shapes. However, with the emergence of large-scale datasets, the efficiency of graph clustering algorithms has become a significant concern. As a…
View article: Editorial: Vicinagearth Safety
Editorial: Vicinagearth Safety Open
As the global economy progresses and environmental challenges intensify, the risks to human safety escalate. In response to these mounting concerns, the Vicinagearth Safety (VS) framework has been introduced. This framework encompasses low…
View article: A novel hybrid adaptive differential evolution for global optimization
A novel hybrid adaptive differential evolution for global optimization Open
Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mu…
View article: PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting
PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting Open
The self-attention mechanism in Transformer architecture, invariant to sequence order, necessitates positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positional embeddings restricts th…
View article: Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping Open
Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within this domain. Doubly stochastic symmetric similarity graphs provide numerous benefits for clusterin…
View article: Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method Open
Ensemble learning is a method that leverages weak learners to produce a strong learner. However, obtaining a large number of base learners requires substantial time and computational resources. Therefore, it is meaningful to study how to a…
View article: Learning a Subspace and Clustering Simultaneously with Manifold Regularized Nonnegative Matrix Factorization
Learning a Subspace and Clustering Simultaneously with Manifold Regularized Nonnegative Matrix Factorization Open
With the incredible growth of high-dimensional data such as microarray gene expression data and web blogs from internet, the researchers are desirable to develop new clustering techniques to address the critical problem created by irreleva…
View article: Adaptive Fuzzy C-Means with Graph Embedding
Adaptive Fuzzy C-Means with Graph Embedding Open
Fuzzy clustering algorithms can be roughly categorized into two main groups: Fuzzy C-Means (FCM) based methods and mixture model based methods. However, for almost all existing FCM based methods, how to automatically selecting proper membe…
View article: Simple Multigraph Convolution Networks
Simple Multigraph Convolution Networks Open
Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators. To alleviate this problem, this paper prop…
View article: Joint Structured Bipartite Graph and Row-Sparse Projection for Large-Scale Feature Selection
Joint Structured Bipartite Graph and Row-Sparse Projection for Large-Scale Feature Selection Open
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 process: constructing a graph with data-to-data affinities or a bi…
View article: Robust Capped lp-Norm Support Vector Ordinal Regression
Robust Capped lp-Norm Support Vector Ordinal Regression Open
Ordinal regression is a specialized supervised problem where the labels show an inherent order. The order distinguishes it from normal multi-class problem. Support Vector Ordinal Regression, as an outstanding ordinal regression model, is w…
View article: Multi-Class Support Vector Machine with Maximizing Minimum Margin
Multi-Class Support Vector Machine with Maximizing Minimum Margin Open
Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximizing the "margin", which represents the minimum distance be…
View article: Simple Multigraph Convolution Networks
Simple Multigraph Convolution Networks Open
Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators. To alleviate this problem, this paper prop…