References
Papers
Radovanović+, 2010
Miloš Radovanović, Alexandros Nanopoulos, and Mirjana Ivanović.
Hubs in space: Popular nearest neighbors in high-dimensional data. Journal of Machine Learning Research, 11:2487–2531, 2010.
Shigeto+, 2015
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto.
Ridge regression, hubness, and zero-shot learning.
Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), pages 135–151, 2015.
Suzuki+, 2013
Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, Marco Saerens, and Kenji Fukumizu.
Centering similarity measures to reduce hubs.
In Proceedings of the 2013 Confer- ence on Empirical Methods in Natural Language Processing (EMNLP), pages 613–623, 2013.
Larochelle+, 2008
Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio.
Zero-data learning of new tasks.
In Proceedings of the 23rd National Conference on Artificial Intelligence (AAAI), pages 646–651, 2008.
Dinu and Baroni, 2015
Georgiana Dinu and Marco Baroni.
Improving zero-shot learning by mitigating the hubness problem.
In Workshop Track on International Conference of Learning Representaion, 2015. URL http://arxiv.org/abs/1412.6568.
Lazaridou+, 2015
Angeliki Lazaridou, Elia Bruni, and Marco Baroni.
Hubness and pollution: Delving into cross-space mapping for zero-shot learning.
In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL '15), pages 270-280, 2015.
Shigeto+, 2016
Yutaro Shigeto, Masashi Shimbo, and Yuji Matsumoto.
A fast and easy regression technique for k-NN classification without using negative pairs.
Advances in Knowledge Discovery and Data Mining (PAKDD), pages 17-29, 2017.
Schnitzer+, 2012
Dominik Schnitzer, Arthur Flexer, Markus Schedl, and Gerhard Widmer.
Local and global scaling reduce hubs in space.
Journal of Machine Learning Research, 13:2871–2902, 2012.
Suzuki+, 2012
Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, Yuji Matsumoto and Marco Saerens.
Investigating the effectiveness of laplacian-based kernels in hub reduction.
In Proceeding of the 26th AAAI Conference on Artificial Intelligence (AAAI), pages 1112-1118, 2012.
Hara+, 2015
Kazuo Hara, Ikumi Suzuki, Masashi Shimbo, Kei Kobayashi, Kenji Fukumizu, and Miloš Radovanović.
Localized centering: Reducing hubness in large-sample data.
In Proceeding of the 29th AAAI Conference on Artificial Intelligence (AAAI), pages 2645-2651, 2015.
Radovanović+, 2009
Miloš Radovanović, Alexandros Nanopoulos, and Mirjana Ivanović.
On the existence of obstinate results in vector space models.
In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 186–193, 2009.
Tomašev+, 2013
Nenad Tomašev, Miloš Radovanović, Dunja Mladenić, and Mirjana Ivanović.
Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification.
International Journal of Machine Learning and Cybernetics, 5:445–458, 2013.
Tomašev+, 2011
Nenad Tomašev, Miloš Radovanović, Dunja Mladenić, and Mirjana Ivanović.
A probabilistic approach to nearest neighbor classification: Naive hubness bayesian kNN.
In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM), pages 2173-2176, 2011.
Tomašev+, 2015
Nenad Tomašev, Krisztian Buza, Kristof Marussy, Piroska B. Kis.
Hubness-aware classication, instance selection and feature construction: Survey and extensions to time-series.
Feature Selection for Data and Pattern Recognition, pages 231-262, 2015.
Slides
Suzuki, 2014
The effect of data centering on k-nearest neighbor -Centering similarity measures-.
Ikumi Suzuki, 2014.
Radovanović, 2017
Hubs in nearest-neighbor graphs: Origins, applications and challenges (version 6).
Miloš Radovanović, 2017.
Hub miner: Hubness-aware machine learning
https://github.com/datapoet/hubminer
https://github.com/OFAI/hub-toolbox-python3
PyHubs
http://biointelligence.hu/pyhubs/
Our code
https://github.com/yutaro-s/ECMLPKDD2015