Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Lu, Run‐kun"'
Publikováno v:
Neural Computing and Applications(2021)
Real world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance. In this paper, we propose a Gaussian Mixture Discriminant Analysis (GMDA) with noisy lab
Externí odkaz:
http://arxiv.org/abs/2201.10242
Publikováno v:
Applied Intelligence (2021)
Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literature in this area ignore the following three issues: (1) they think litt
Externí odkaz:
http://arxiv.org/abs/2201.07383
Publikováno v:
Neural Computing and Applications(2020)
Over recent decades have witnessed considerable progress in whether multi-task learning or multi-view learning, but the situation that consider both learning scenes simultaneously has received not too much attention. How to utilize multiple views lat
Externí odkaz:
http://arxiv.org/abs/2201.05829
Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and one of the significant directions of multi-view learning is sub-space l
Externí odkaz:
http://arxiv.org/abs/2201.02978
Multi-view learning accomplishes the task objectives of classification by leverag-ing the relationships between different views of the same object. Most existing methods usually focus on consistency and complementarity between multiple views. But not
Externí odkaz:
http://arxiv.org/abs/2201.04726
Multi-view subspace clustering always performs well in high-dimensional data analysis, but is sensitive to the quality of data representation. To this end, a two stage fusion strategy is proposed to embed representation learning into the process of m
Externí odkaz:
http://arxiv.org/abs/2201.01050
Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clu
Externí odkaz:
http://arxiv.org/abs/2201.00171
Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complementarity, most existing approaches only can find representations with sin
Externí odkaz:
http://arxiv.org/abs/2201.00168
In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view's dynamic contribution obtained b
Externí odkaz:
http://arxiv.org/abs/2112.12506
Autor:
Xin, Jiaxing, Chen, Jinzhong, He, Renyang, Li, Rui, Li, Xiaolong, Liu, Chang, Lu, Run-kun, Su, Zhengda, Han, Wenbo
Publikováno v:
In Ocean Engineering 15 May 2024 300
