A linear space adjustment by mapping data into an intermediate space and keeping low level data structures. Issue 4 (4th July 2021)

• Record Type: Journal Article
• Authors: Fu, Weiqing; Parvin, Hamid; Mahmoudi, Mohammad Reza; Tuan, Bui Anh; Pho, Kim-Hung
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 33:Issue 4(2021)
• Page Start: 637
• Page End: 657
• Publication Date: 2021-07-04
• Subjects: Space Adjustment -- optimisation -- structure Maintenance -- classification; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2020.1764634
• Languages: English
• ISSNs: 0952-813X

Abstract

ABSTRACT: One of the most important assumptions in machine learning tasks is the fact that training data points and test data points are extracted from the same distribution. However, this paper assumes the situation in which this fact does no longer hold. Therefore, a task named space adjustment, through which the distribution of the data points in the training-data space and the distribution of the data points in the test-data space become identical, is inevitable. Hereby, authors propose a linear mapping for the space adjustment task in the paper. It considers four approaches for preserving localities among data samples during the space adjustment. Each approach is defined based on a different locality concept. Considering all locality concepts in an objective function, authors transform the space adjustment into an optimisation problem. The paper proposes to optimise the corresponding objective function by an iterative approach. Empirical study shows that the proposed method outperforms the baseline methods. To do experiments, authors employ a large number of real-world datasets.