We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses ...

Clustering methods provide a powerful tool for the exploratory analysis of high-dimension, low—sample size (HDLSS) data sets, such as gene expression microarray data. A fundamental statistical issue ...

In order to evaluate a dataset of over 11 million cells from a study of dengue fever, Yale researchers developed a cutting-edge neural network that recognizes and represents patterns in large datasets ...

In this paper, we introduce a new method to analyse HIV using a combination of autoencoder networks and genetic algorithms. The proposed method is tested on a set of demographic properties of ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...