Transfer dimensionality reduction by Gaussian process in parallel

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

研究成果: ジャーナルへの寄稿学術誌査読


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, we attempt to learn a more informative mapping function between the original data and the reduced data by Gaussian process that behaves more appropriately than other parametric regression methods due to its less parametric characteristic. In our proposal, we firstly convert 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.

ジャーナルKnowledge and Information Systems
出版ステータス出版済み - 3月 2014

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム
  • 人間とコンピュータの相互作用
  • ハードウェアとアーキテクチャ
  • 人工知能


「Transfer dimensionality reduction by Gaussian process in parallel」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。