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Unsupervised Machine Learning

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Disease Trajectories and End-of-Life Care for Dementias: Latent Topic Modeling and Trend Analysis Using Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in the U.S., little is known about the course of these diseases and what care dementia patients receive in their final years of life. Using a large volum...

A density-based competitive data stream clustering network with self-adaptive distance metric.

Neural networks : the official journal of the International Neural Network Society
Data stream clustering is a branch of clustering where patterns are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data ...

Keratoconus severity identification using unsupervised machine learning.

PloS one
We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from ...

Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images.

IEEE transactions on medical imaging
Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviati...

Unsupervised feature extraction by low-rank and sparsity preserving embedding.

Neural networks : the official journal of the International Neural Network Society
Manifold based feature extraction has been proved to be an effective technique in dealing with the unsupervised classification tasks. However, most of the existing works cannot guarantee the global optimum of the learned projection, and they are sens...

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.

IEEE transactions on medical imaging
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation o...

High-Fidelity Monocular Face Reconstruction Based on an Unsupervised Model-Based Face Autoencoder.

IEEE transactions on pattern analysis and machine intelligence
In this work, we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network...

Unsupervised and real-time spike sorting chip for neural signal processing in hippocampal prosthesis.

Journal of neuroscience methods
BACKGROUND: Damage to the hippocampus will result in the loss of ability to form new long-term memories and cognitive disorders. At present, there is no effective medical treatment for this issue. Hippocampal cognitive prosthesis is proposed to repla...

Interactive reservoir computing for chunking information streams.

PLoS computational biology
Chunking is the process by which frequently repeated segments of temporal inputs are concatenated into single units that are easy to process. Such a process is fundamental to time-series analysis in biological and artificial information processing sy...