Gaussian process for dimensionality reduction in transfer learning

Bin Tong, Junbin Gao, Nguyen Huy Thach, Einoshin Suzuki

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

5 引用 (Scopus)

抜粋

Dimensionality reduction has been considered as one of the most significant tools for data analysis. In general, supervised information is helpful for dimensionality reduction. However, in typical real applications, supervised information in multiple source tasks may be available, while the data of the target task are unlabeled. An interesting problem of how to guide the dimensionality reduction for the unlabeled target data by exploiting useful knowledge, such as label information, from multiple source tasks arises in such a scenario. In this paper, we propose a new method for dimensionality reduction in the transfer learning setting. Unlike traditional paradigms where the useful knowledge from multiple source tasks is transferred through distance metric, our proposal firstly converts the dimensionality reduction problem into integral regression problems in parallel. Gaussian process is then employed to learn the underlying relationship between the original data and the reduced data. Such a relationship can be appropriately transferred to the target task by exploiting the prediction ability of the Gaussian process model and inventing different kinds of regularizers. Extensive experiments on both synthetic and real data sets show the effectiveness of our method.

元の言語英語
ホスト出版物のタイトルProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
出版者Society for Industrial and Applied Mathematics Publications
ページ783-794
ページ数12
ISBN(印刷物)9780898719925
DOI
出版物ステータス出版済み - 2011
イベント11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, 米国
継続期間: 4 28 20114 30 2011

出版物シリーズ

名前Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

その他

その他11th SIAM International Conference on Data Mining, SDM 2011
米国
Mesa, AZ
期間4/28/114/30/11

All Science Journal Classification (ASJC) codes

  • Software

フィンガープリント Gaussian process for dimensionality reduction in transfer learning' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Tong, B., Gao, J., Thach, N. H., & Suzuki, E. (2011). Gaussian process for dimensionality reduction in transfer learning. : Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 (pp. 783-794). (Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972818.67