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
AUTHORS (4)
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 ....