Ridge Regression, Hubness, and Zero-Shot Learning

Autor:	Shigeto, Yutaro, Suzuki, Ikumi, Hara, Kazuo, Shimbo, Masashi, Matsumoto, Yuji
Rok vydání:	2015
Předmět:	Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step. Assuming a simple data model, we prove that the proposed approach indeed reduces hubness. This was verified empirically on the tasks of bilingual lexicon extraction and image labeling: hubness was reduced with both of these tasks and the accuracy was improved accordingly. Comment: To be presented at ECML/PKDD 2015
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1507.00825 Zobrazit plný text záznamu View this record from Arxiv