Ridge Regression, Hubness, and Zero-Shot Learning

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 i...

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Bibliographic Details
Published inMachine Learning and Knowledge Discovery in Databases pp. 135 - 151
Main Authors Shigeto, Yutaro, Suzuki, Ikumi, Hara, Kazuo, Shimbo, Masashi, Matsumoto, Yuji
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2015
SeriesLecture Notes in Computer Science
Subjects
Canonical Correlation Analysis
Neighbor Search
Ridge Regression
Target Object
Target Space
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Summary: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.
ISBN:3319235273
9783319235271
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-23528-8_9