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Feature-aware unsupervised learning with joint variational attention and automatic clustering
- Source :
- ICPR
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Deep clustering aims to cluster unlabeled real-world samples by mining deep feature representation. Most of existing methods remain challenging when handling high -dimensional data and simultaneously exploring the complementarity of deep feature representation and clustering. In this paper, we propose a novel Deep Variational Attention Encoder-decoder for Clustering (DVAEC). Our DVAEC improves the representation learning ability by fusing variational attention. Specifically, we design a feature-aware automatic clustering module to mitigate the unreliability of similarity calculation and guide network learning. Besides, to further boost the performance of deep clustering from a global perspective, we define a joint optimization objective to promote feature representation learning and automatic clustering synergistically. Extensive experimental results show the promising performance achieved by our DVAEC on six datasets comparing with several popular baseline clustering methods.
- Subjects :
- Computer science
business.industry
Perspective (graphical)
02 engineering and technology
010501 environmental sciences
Machine learning
computer.software_genre
01 natural sciences
ComputingMethodologies_PATTERNRECOGNITION
Similarity (network science)
Feature (computer vision)
0202 electrical engineering, electronic engineering, information engineering
Task analysis
Unsupervised learning
020201 artificial intelligence & image processing
Artificial intelligence
Cluster analysis
business
Representation (mathematics)
computer
Feature learning
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 25th International Conference on Pattern Recognition (ICPR)
- Accession number :
- edsair.doi...........a5801be3b2bb53abf896775af8f68ddd