This work describes a machine learning approach to the feature extraction from multi-dimensional microscopy datasets. By this unsupervised learning manner, the data (pixels) can be classified into several groups that represent important features inherent in the dataset.
pip install drca
@article{ryu2021drca,
author = {Ryu, J. and Kim, H. and Kim, R. M. and Kim, S. and Jo, J. and Lee, S. and Nam, K. T. and Joo, Y. C. and Yi, G. C. and Lee, J. and Kim, M.},
title = {Dimensionality reduction and unsupervised clustering for EELS-SI},
journal = {Ultramicroscopy},
volume = {231},
pages = {113314},
year = {2021},
doi = {10.1016/j.ultramic.2021.113314}}
Jinseok Ryu, Ph.D. (jinseuk56@gmail.com)