id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-332086-hnn00byf	Dolgikh, S.	Identifying Explosive Cases with Unsupervised Machine Learning	2020-05-22		.txt	text/plain	2670	133	45	An analysis of a combined dataset of Wave 1 and 2 cases, aligned at approximately Local Time Zero + 2 months with unsupervised machine learning methods such as PCA and deep autoencoder dimensionality reduction allows to clearly separate milder background cases from those with more rapid and aggressive onset of the epidemics. The methodology is based on processing the input data expressed as a set of observable parameters that were identified and described in the study with unsupervised machine learning methods to identify and extract a smaller set of the most informative components. In many cases, evaluating distributions of data in the representations of informative components such as principal components in PCA or dimensionality reduction with neural network autoencoder models allows to identify and separate classes in the data by essential characteristics that can be linked to the outcome.	./cache/cord-332086-hnn00byf.txt	./txt/cord-332086-hnn00byf.txt