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Unsupervised Machine Learning

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Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning.

Journal of applied clinical medical physics
PURPOSE: The deformable registration of 3D chest computed tomography (CT) images is one of the most important tasks in the field of medical image registration. However, the nonlinear deformation and large-scale displacement of lung tissues caused by ...

A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts.

Sensors (Basel, Switzerland)
Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for th...

Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm.

Sensors (Basel, Switzerland)
Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was...

Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.

Gait & posture
BACKGROUND: Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. ...

Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers.

IEEE/ACM transactions on computational biology and bioinformatics
Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for a...

Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: The burst suppression pattern in clinical electroencephalographic (EEG) recordings is an important diagnostic tool because of its association with comas of various etiologies, as with hypoxia, drug related intoxication or deep anesthesia. ...

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

eLife
For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrai...

Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints.

Neural networks : the official journal of the International Neural Network Society
We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine formulation for kernel PCA by orthogonality constraints on the latent...

Representation learning of resting state fMRI with variational autoencoder.

NeuroImage
Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model ...

Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a "Chatty Device".

Sensors (Basel, Switzerland)
To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. "Chatty Factories" is a concept that explores the transformative potential of placing IoT-enabled...