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Unsupervised Subspace Extraction via Deep Kernelized Clustering
- Source :
- ACM Transactions on Knowledge Discovery from Data. 16:1-15
- Publication Year :
- 2021
- Publisher :
- Association for Computing Machinery (ACM), 2021.
-
Abstract
- Feature extraction has been widely studied to find informative latent features and reduce the dimensionality of data. In particular, due to the difficulty in obtaining labeled data, unsupervised feature extraction has received much attention in data mining. However, widely used unsupervised feature extraction methods require side information about data or rigid assumptions on the latent feature space. Furthermore, most feature extraction methods require predefined dimensionality of the latent feature space,which should be manually tuned as a hyperparameter. In this article, we propose a new unsupervised feature extraction method called Unsupervised Subspace Extractor ( USE ), which does not require any side information and rigid assumptions on data. Furthermore, USE can find a subspace generated by a nonlinear combination of the input feature and automatically determine the optimal dimensionality of the subspace for the given nonlinear combination. The feature extraction process of USE is well justified mathematically, and we also empirically demonstrate the effectiveness of USE for several benchmark datasets.
- Subjects :
- Hyperparameter
General Computer Science
business.industry
Computer science
Feature vector
Feature extraction
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Feature (computer vision)
0202 electrical engineering, electronic engineering, information engineering
Unsupervised learning
020201 artificial intelligence & image processing
Artificial intelligence
business
Cluster analysis
Subspace topology
Curse of dimensionality
Subjects
Details
- ISSN :
- 1556472X and 15564681
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- ACM Transactions on Knowledge Discovery from Data
- Accession number :
- edsair.doi...........8793d2c643e5070233774c1e0383a3bb