Improved sampling strategy for representative set construction
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Sarkar, Debangsha Kusum
issued: 2022-01-06T19:36:23Z
issued: 2022
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Active learning solves machine learning problems where acquiring labels for the data is costly. A representative subset helps active learning by selecting the most useful subset of a complete data set and querying the labels of the examples present in it. Various sampling strategies exist for the construction of this type of representative subset. However, the state-of-the-art approaches for subset sampling aim to construct a representative subset where the cost of optimization on the subset is comparable to that of the complete data set. They do not attempt to capture the underlying distribution of the data, which we believe is the most important part of representative sampling. This thesis proposes an adaptation of the sigma point sampling (SPS) technique from unscented transformation (UT) for subset sampling. Unscented transformation (UT) has shown to be very effective in non-linear transformation modeling in object tracking and robotics. We show in this thesis that when combined with the Gaussian mixture model, sigma points can estimate the true statistics of an unknown distribution of data with very few samples. The sigma point sampling approach being parameterized gives better control over the sampling process than the non-parameterized sampling techniques. We use the representative subset for active learning on an existing data set. As a practical application of this novel sampling technique, we use it for Active Transfer Learning (ATL) on autoclave processing data to optimize the manufacturing of high-quality aerospace composite parts. The sampling technique is tested on both classification and regression problems in a pool-based active learning scenario. After comparing our approach to the state-of-the-art sampling techniques, We show that sigma point sampling either outperforms or matches their performance. We also test the sensitivity of the parameters in the sigma point sampling. The success of this sampling technique is very significant for future devel…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/80545
note: Improved Sampling Strategy forRepresentative Set ConstructionbyDebangsha Kusum Sarkar,MDS., The University of British Columbia, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)January 2022© Debangsha Kusum Sarkar, 2022The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Improved Sampling Strategy for Representative Set Constructionsubmitted by Debangsha Kusum Sarkar in partial fulfillment of the re-quirements of the degree of Master of Science.Dr. Apurva Narayan, Irving K. Barber Faculty of ScienceSupervisorDr. Ramon Lawrence, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Jeffrey Andrews, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Mahmudur Fatmi, School of EngineeringUniversity Examinerii“The great thing about predictions is that you can be wrong.”— Dr. Chris Wiggins (Columbia University)iiAbstractActive learning solves machine learning problems where acquiring labels forthe data is costly. A representative subset helps active learning by selectingthe most useful subset of a complete data set and querying the labels of theexamples present in it. Various sampling strategies exist for the construc-tion of this type of representative subset. However, the state-of-the-art ap-proaches for subset sampling aim to construct a representative subset wherethe cost of optimization on the subset is comparable to that of the com-plete data set. They do not attempt to capture the underlying distributionof the data, which we believe is the most important part of representativesampling. This thesis proposes an adaptation of the sigma point sampling(SPS) technique from unscented transformation (UT) for subset sampling.Unscented transformation (UT) has shown to be very effective in non-lineartransform…
has type: Thesis/Dissertation
date issued: 2022-02
is shown at: 10.14288/1.0406204
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Narayan, Apurva
type: Text
identifier URI: http://hdl.handle.net/2429/80545