Abstract
This paper presents a novel procedure to train Extreme Learning Machine models on datasets with missing values. In effect, a separate model is learned to classify every sample in the test set, however, this is accomplished in an efficient manner which does not require accessing the training data repeatedly. Instead, a sparse structure is imposed on the input layer weights, which enables calculating the necessary statistics in the training phase. An application to predicting the progression of Huntington's disease from brain scans is presented. Experimental comparisons show promising results equivalent to the state of the art in machine learning with incomplete data.
Original language | English |
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Title of host publication | 10th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2017 |
Number of pages | 4 |
Volume | Part F128530 |
Publisher | ACM - Association for Computing Machinery |
Publication date | 21.06.2017 |
Pages | 189-192 |
ISBN (Electronic) | 978-1-4503-5227-7 |
DOIs | |
Publication status | Published - 21.06.2017 |
MoE publication type | A4 Article in conference proceedings |
Event | 10th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2017 - Island of Rhodes, Greece Duration: 21.06.2017 → 23.06.2017 |
Keywords
- 512 Business and Management