Zobrazeno 1 - 10
of 1 725
pro vyhledávání: '"Nie, Feiping"'
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 performs un
Externí odkaz:
http://arxiv.org/abs/2411.01792
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 necessitate ca
Externí odkaz:
http://arxiv.org/abs/2411.01780
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-Grained Ensembl
Externí odkaz:
http://arxiv.org/abs/2409.12849
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 the Transform
Externí odkaz:
http://arxiv.org/abs/2408.10483
Autor:
Yuan, Jinghui, Zeng, Chusheng, Xie, Fangyuan, Cao, Zhe, Chen, Mulin, Wang, Rong, Nie, Feiping, Yuan, Yuan
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 clustering problems
Externí odkaz:
http://arxiv.org/abs/2408.02932
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 achieve the
Externí odkaz:
http://arxiv.org/abs/2408.02936
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 membership degre
Externí odkaz:
http://arxiv.org/abs/2405.13427
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 widely used
Externí odkaz:
http://arxiv.org/abs/2404.16616
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 proposes a Simp
Externí odkaz:
http://arxiv.org/abs/2403.05014
In the last decade, embedded multi-label feature selection methods, incorporating the search for feature subsets into model optimization, have attracted considerable attention in accurately evaluating the importance of features in multi-label classif
Externí odkaz:
http://arxiv.org/abs/2403.00307