Enhancing Speed of Gaussian Processes.

Date of Submission

December 2020

Date of Award

Winter 12-12-2021

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Advance Computing and Microelectronics Unit (ACMU-Kolkata)


Chakraborty, Sourav (ISRU-Kolakata; ISI)

Abstract (Summary of the Work)

Gaussian Processes are used in supervised learning. They have been in the world of machine learning for quite some time, dealing with complex data sets where parametric methods fail. While calculating the gaussian distribution function for a large feature vector, we need a matrix inversion algorithm which has high run time complexity O(n3) and space complexity O(n2). To increase its performance, subset sampling is an important technique used, one method was described in the paper Fast Gaussian Process Regression for Big Data by Sourish Das, Sasanka Roy, Rajiv Sambasivan. It described an algorithm involving combined estimates from models developed using subsets sampled uniformly, much similar to bootstrap sampling. But as a drawback it has been found that the method doesn't work well for all kinds of data. The results developed were based on synthetic data only. In our work we shall provide a different sampling technique. We put weights on the points and sample accordingly. This is thought to be a better approach if the weights are chosen wisely. Empirical results to establish our idea have been provided.


ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28842685

Control Number


Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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