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Open AccessArticle

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea
Author to whom correspondence should be addressed.
Academic Editor: Andreas Holzinger
Entropy 2016, 18(6), 222;
Received: 19 April 2016 / Revised: 1 June 2016 / Accepted: 2 June 2016 / Published: 7 June 2016
Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input-output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input-output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application-dependent threshold. This output- and standard deviation-based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning-tree (BSP-tree) and k-means algorithms. We apply the new approach to data-driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state-of-the-art sample selection algorithms for regression tasks. Four kinds of haptic related behavior–force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output-approximation quality and computational complexity. View Full-Text
Keywords: sample selection; regression; data-driven modeling; haptic feedback sample selection; regression; data-driven modeling; haptic feedback
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.53938
    Description: The datasets used in the current study are available at the the above mentioned location.
MDPI and ACS Style

Abdulali, A.; Hassan, W.; Jeon, S. Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling. Entropy 2016, 18, 222.

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