Multi-task manifold learning using hierarchical modeling for insufficient samples
説明
In this paper, we propose a method for multi-task manifold learning. For a set of tasks of dimensionality reduction, the aim of the method is to model each given dataset as a manifold, and map it to a low-dimensional space. For this purpose, we use a hierarchical manifold modeling approach. Thus, while each data distribution is represented by a manifold model, the obtained models are further modeled by a higher-order manifold in a function space. The higher-order model mediates the information transfer between tasks, and as a result, the performance of each task is improved. The results of simulations show that the proposed method can estimate manifolds approximately, even in cases in which a tiny number of samples are provided for each task.
収録刊行物
-
- Lecture Notes in Computer Science
-
Lecture Notes in Computer Science 11303 388-398, 2018-11-18
Springer
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1050295724153719424
-
- HANDLE
- 10228/00009160
-
- 本文言語コード
- en
-
- 資料種別
- journal article
-
- データソース種別
-
- IRDB
- OpenAIRE