文献阅读(part3)--Self-taught Clustering

最新推荐文章于 2024-06-26 09:47:32 发布

GUI Research Group

最新推荐文章于 2024-06-26 09:47:32 发布

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分类专栏：机器学习文章标签：聚类深度聚类机器学习

本文链接：https://blog.youkuaiyun.com/m0_37422217/article/details/109064773

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本文介绍了自学习聚类，一种无监督迁移学习任务，利用大量辅助无标记数据增强小规模目标数据的聚类性能。通过共聚类算法，发掘辅助数据中的特征来改进目标数据的聚类表示，实验表明，这种方法在图像聚类等场景下能显著优于传统聚类方法。

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Abstract

This paper focuses on a new clustering task, called self-taught clustering. Self-taught clustering is an instance of unsupervised transfer learning, which aims at clustering a small collection of target unlabeled data with the help of a large amount of auxiliary unlabeled data. The target and auxiliary data can be different in topic distribution. We show that even when the target data are not sufficient to allow effective learning of a high quality feature representation, it is possible to learn the useful features with the help of the auxiliary data on which the target data can be clustered effectively. We propose a co-clustering based self-taught clustering algorithm

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