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Journal article
Deep Clustering for Improved Inter-Cluster Separability and Intra-Cluster Homogeneity with Cohesive Loss
IEICE transactions on information and systems, v E104D(5), pp 776-780
01 May 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
To date, many studies have employed clustering for the classification of unlabeled data. Deep separate clustering applies several deep learning models to conventional clustering algorithms to more clearly separate the distribution of the clusters. In this paper, we employ a convolutional autoencoder to learn the features of input images. Following this, k-means clustering is conducted using the encoded layer features learned by the convolutional autoencoder. A center loss function is then added to aggregate the data points into clusters to increase the intra-cluster homogeneity. Finally, we calculate and increase the inter-cluster separability. We combine all loss functions into a single global objective function. Our new deep clustering method surpasses the performance of existing clustering approaches when compared in experiments under the same conditions.
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Details
- Title
- Deep Clustering for Improved Inter-Cluster Separability and Intra-Cluster Homogeneity with Cohesive Loss
- Creators
- Byeonghak Kim - Korea UniversityMurray Loew - George Washington UniversityDavid K. Han - Drexel UniversityHanseok Ko - School of Electrical Engineering
- Publication Details
- IEICE transactions on information and systems, v E104D(5), pp 776-780
- Publisher
- Ieice-Inst Electronics Information Communications Eng
- Number of pages
- 5
- Grant note
- GWU (KU-GWU Joint Research Fund) HG19C0682 / Government-wide R&D Fund project for infectious disease research (GFID), Republic of Korea
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000646183700029
- Scopus ID
- 2-s2.0-85106722001
- Other Identifier
- 991019168733604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering