.png){kind=icon}
返回
Large scale multi-label learning using Gaussian processes
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-021-05952-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization. Specifically, we apply doubly stochastic variational inference that sub-samples data instances and classes which allows us to cope with Big Data. Furthermore, we show it is possible and beneficial to optimize over inducing points, using gradient-based methods, even in very high dimensional input spaces involving up to hundreds of thousands of dimensions. We demonstrate the usefulness of our approach on several real-world large-scale multi-label learning problems.
Keyword:
Multi-label learning
Gaussian process
Variational inference
Bayesian nonparametrics
期刊
IF:
2.9
论文数:
2.7K
被引数:
3.4W
机构
引用论文
Can CD34 discriminate between benign and malignant hepatocytic lesions in fine-needle aspirates and thin core biopsies?.png)
Cancer
OAAI
CancerIF0
OAAI分享收藏Definitive evidence that a single N-glycan among three glycans on inducible costimulator is required for proper protein trafficking and ligand binding明确的证据表明，在可诱导的共刺激物上的三个聚糖中，单个N-聚糖对于适当的蛋白质运输和配体结合是必需的
PREAI
分享收藏分享收藏
查看更多内容