.png){kind=icon}
返回
New Scalable and Efficient Online Pairwise Learning Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2023.3299756.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Pairwise learning is an important machine-learning topic with many practical applications. An online algorithm is the first choice for processing streaming data and is preferred for handling large-scale pairwise learning problems. However, existing online pairwise learning algorithms are not scalable and efficient enough for large-scale high-dimensional data, because they were designed based on singly stochastic gradients. To address this challenging problem, in this article, we propose a dynamic doubly stochastic gradient algorithm (D2SG) for online pairwise learning. Especially, only the time and space complexities of O(d) are needed for incorporating a new sample, where d is the dimensionality of data. This means that our D2SG is much faster and more scalable than the existing online pairwise learning algorithms while the statistical accuracy can be guaranteed through our rigorous theoretical analysis under standard assumptions. The experimental results on a variety of real-world datasets not only confirm the theoretical result of our new D2SG algorithm, but also show that D2SG has better efficiency and scalability than the existing online pairwise learning algorithms.
Keyword:
Doubly stochastic gradient algorithm
online learning
pairwise learning
statistical accuracy
期刊
IF:
8.9
论文数:
7.5K
被引数:
7.2W
机构
引用论文
分享收藏Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval用于多特征图像检索的社会邻域图和多图融合排序
PREAI
分享收藏N-methyl-d-aspartate-induced nitric oxide release: An in vivo microdialysis study.png)
Neuroscience
NeuroscienceIF0
PREAI
分享收藏
查看更多内容