AIMC Topic: Unsupervised Machine Learning

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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...

Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning.

Sleep medicine
OBJECTIVE: This study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique.

Populational and individual information based PET image denoising using conditional unsupervised learning.

Physics in medicine and biology
Our study aims to improve the signal-to-noise ratio of positron emission tomography (PET) imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily appli...

An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor.

Sensors (Basel, Switzerland)
Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused...

An active learning approach for clustering single-cell RNA-seq data.

Laboratory investigation; a journal of technical methods and pathology
Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover...

Exploring the potential of utilizing unsupervised machine learning for urban drainage sensor placement under future rainfall uncertainty.

Journal of environmental management
Recently, advanced informatics and sensing techniques show promise of enabling a new generation of smart stormwater systems, where real-time sensors are deployed to detect flooding hotspots. Existing stormwater design criteria assume that historical ...

Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.

Annals of the rheumatic diseases
Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support...