Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction

FOS: Computer and information sciences 0301 basic medicine Computer Science - Machine Learning Artificial intelligence Semi-Supervised Learning Unsupervised Learning Evolutionary biology Machine Learning (stat.ML) Pattern recognition (psychology) Unsupervised learning Pattern Recognition, Automated Machine Learning (cs.LG) Anomaly Detection in High-Dimensional Data 03 medical and health sciences Deep Learning Cluster analysis Clustering Analysis Statistics - Machine Learning Artificial Intelligence Meta-Learning Machine learning Cluster Analysis Data mining Biology Geography Deep learning Autoencoder Centroid Computer science Process (computing) Operating system Advances in Transfer Learning and Domain Adaptation Function (biology) Human Action Recognition and Pose Estimation Computer Science Physical Sciences Computer Vision and Pattern Recognition Benchmark (surveying) Algorithms Geodesy
DOI: 10.1016/j.neunet.2020.07.005 Publication Date: 2020-07-11T02:36:39Z
ABSTRACT
<p>In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static during the training process. The absence of concrete supervision suggests that smooth dynamics should be integrated. Compared to classical static cost functions, dynamic objective functions allow to better make use of the gradual and uncertain knowledge acquired through pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a novel model for deep clustering that overcomes a clustering-reconstruction trade-off, by gradually and smoothly eliminating the reconstruction objective function in favor of a construction one. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to the most relevant deep clustering methods.</p>
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (74)
CITATIONS (61)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....