Benchmarking a tensor network-based machine learning algorithm for supervised image classification
Abstract
Real-world data is often scarce and the careful setting of hyperparameters to efficiently train machine learning algorithms is necessary. In this study the role of bond dimension on the performance of an existing tensor network-based machine learning program was benchmarked on the MNIST and fashion-MNIST data sets. In particular, the effects of bond dimension on the accuracy and execution times of the algorithm were investigated. Results show that a bond dimension of less than 100 is sufficient to reach an acceptable classification accuracy, and increasing the bond dimension further extends the execution time and consumes prohibitive amounts of memory without providing a reasonable improvement in accuracy.
Downloads
Published
Issue
Section
License
By submitting their manuscript to the Samahang Pisika ng Pilipinas (SPP) for consideration, the Authors warrant that their work is original, does not infringe on existing copyrights, and is not under active consideration for publication elsewhere.
Upon acceptance of their manuscript, the Authors further agree to grant SPP the non-exclusive, worldwide, and royalty-free rights to record, edit, copy, reproduce, publish, distribute, and use all or part of the manuscript for any purpose, in any media now existing or developed in the future, either individually or as part of a collection.
All other associated economic and moral rights as granted by the Intellectual Property Code of the Philippines are maintained by the Authors.
