An efficient algorithm for large-scale quasi-supervised learning

Karacali Bilge

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An efficient algorithm for large-scale quasi-supervised learning

机译：一种有效的大规模准监督学习算法

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摘要

We present a novel formulation for quasi-supervised learning that extends the learning paradigm to large datasets. Quasi-supervised learning computes the posterior probabilities of overlapping datasets at each sample and labels those that are highly specific to their respective datasets. The proposed formulation partitions the data into sample groups to compute the dataset posterior probabilities in a smaller computational complexity. In experiments on synthetic as well as real datasets, the proposed algorithm attained significant reduction in the computation time for similar recognition performances compared to the original algorithm, effectively generalizing the quasi-supervised learning paradigm to applications characterized by very large datasets.

机译：我们提出了一种用于准监督学习的新方法，该方法将学习范式扩展到大型数据集。准监督学习计算每个样本处重叠数据集的后验概率，并标记那些高度针对其各自数据集的后验概率。提出的公式将数据划分为样本组，以较小的计算复杂度来计算数据集的后验概率。在原始数据集和真实数据集上的实验中，与原始算法相比，该算法在相似识别性能方面的计算时间显着减少，从而将准监督学习范式有效地推广到了以大型数据集为特征的应用程序。

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来源
《Pattern Analysis and Applications》 |2016年第2期|311-323|共13页
作者
Karacali Bilge;
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作者单位

Izmir Inst Technol, Dept Elect & Elect Engn, TR-35430 Izmir, Turkey;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Quasi-supervised learning; Posterior probability estimation; Nearest neighbor rule; Large-scale pattern recognition; Transductive inference;

机译：拟监督学习;后验概率估计;最近邻规则;大规模模式识别;传递推理;

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机译：一种有效的大规模准监督学习算法

An efficient algorithm for large-scale quasi-supervised learning

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