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

Abstract

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.

Instance Selection for Classifier Performance Estimation in Meta Learning

“…Recent methods in the field first solve other problems and then use classification, namely, regression problems as in [25][26][27][28][29][30], instance selection in data streams as in [31][32][33], and time series classification [34,35], or build ensembles of instance selection [36][37][38][39][40] and even create meta-learning systems, which automatically adjust a proper instance selection method to a given dataset as in [41,42].…”

Data-Driven Modeling and Rendering of Force Responses from Elastic Tool Deformation

“…However, for the large datasets, the processing time becomes everlasting. The second class of algorithms is based on hierarchical clustering, which recursively partitions the input space of the initial set and selects a single representative sample from each group of partitions [ 46 , 47 ].…”

“…The testing process was repeated ten times where each subset was used for testing once. On each testing iteration, a representative training set of input-output pairs was selected from the whole training data, i.e., data in nine training subsets using the SMASS algorithm introduced in [ 46 ]. The threshold value for the algorithm was selected empirically as suggested.…”

Multi-Objective Evolutionary Instance Selection for Regression Tasks

“…In [ 32 ], an instance selection method for regression was based on recursive data partitioning. The algorithm started with partitioning the input space using the k-means clustering.…”