AIMC Topic: Unsupervised Machine Learning

Clear Filters Showing 401 to 410 of 828 articles

Fast and precise single-cell data analysis using a hierarchical autoencoder.

Nature communications
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical ...

Unsupervised manifold learning of collective behavior.

PLoS computational biology
Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generat...

Unsupervised Cross Domain Person Re-Identification by Multi-Loss Optimization Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Unsupervised cross domain (UCD) person re-identification (re-ID) aims to apply a model trained on a labeled source domain to an unlabeled target domain. It faces huge challenges as the identities have no overlap between these two domains. At present,...

Complementary Pseudo Labels for Unsupervised Domain Adaptation On Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic performance drop when tested on an unseen domain. Existing methods are pri...

Unsupervised learning of Swiss population spatial distribution.

PloS one
The paper deals with the analysis of spatial distribution of Swiss population using fractal concepts and unsupervised learning algorithms. The research methodology is based on the development of a high dimensional feature space by calculating local g...

A pre-training and self-training approach for biomedical named entity recognition.

PloS one
Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training dat...

Building an adaptive interface via unsupervised tracking of latent manifolds.

Neural networks : the official journal of the International Neural Network Society
In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, du...

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches.

Scientific reports
Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification's performance. We introduce a machine-learning method and...

Unsupervised cross-domain named entity recognition using entity-aware adversarial training.

Neural networks : the official journal of the International Neural Network Society
The success of neural network based methods in named entity recognition (NER) is heavily relied on abundant manual labeled data. However, these NER methods are unavailable when the data is fully-unlabeled in a new domain. To address the problem, we p...

Characterization of Antiphospholipid Syndrome Atherothrombotic Risk by Unsupervised Integrated Transcriptomic Analyses.

Arteriosclerosis, thrombosis, and vascular biology
OBJECTIVE: Our aim was to characterize distinctive clinical antiphospholipid syndrome phenotypes and identify novel microRNA (miRNA)-mRNA-intracellular signaling regulatory networks in monocytes linked to cardiovascular disease. Approach and Results:...