AIMC Topic: Unsupervised Machine Learning

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Unsupervised spectral mapping and feature selection for hyperspectral anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Exploring techniques that breakthrough the unknown space or material species is of considerable significance to military and civilian fields, and it is a challenging task without any prior information. Nowadays, the use of material-specific spectral ...

An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features.

Computational and mathematical methods in medicine
The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals....

AD risk score for the early phases of disease based on unsupervised machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.

Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)?

Journal of chemical information and modeling
Coulomb matrix eigenvalues (CMEs) are global 3D representations of molecular structure, which have been previously used to predict atomization energies, prioritize geometry searches, and interpret rotational spectra. The properties of the CME represe...

Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement.

Neural networks : the official journal of the International Neural Network Society
Image-to-image translation has drawn great attention during the past few years. It aims to translate an image in one domain to a target image in another domain. However, three big challenges remain in image-to-image translation: (1) the lack of large...

A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.

PloS one
Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories ...

Cortical surface registration using unsupervised learning.

NeuroImage
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface propert...

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.

Aging
Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathoge...