TY - GEN
T1 - Multi-directional laplacian pyramids for completion of missing data entries
AU - Rabin, Neta
N1 - Publisher Copyright: © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
PY - 2020
Y1 - 2020
N2 - A common pre-processing task in machine learning is handling missing data entries, also known as imputation. Standard techniques use mean values, regression or optimization based techniques for predicting the missing data values. In this paper, a kernel based technique is utilized for imputing data in a multi-scale manner. The construction is based on Laplacian pyramids, which operate on the row and column spaces of the data in several scales. Experimental results demonstrate the approach on publicly available datasets, and highlight its simple computational construction and convergence stability.
AB - A common pre-processing task in machine learning is handling missing data entries, also known as imputation. Standard techniques use mean values, regression or optimization based techniques for predicting the missing data values. In this paper, a kernel based technique is utilized for imputing data in a multi-scale manner. The construction is based on Laplacian pyramids, which operate on the row and column spaces of the data in several scales. Experimental results demonstrate the approach on publicly available datasets, and highlight its simple computational construction and convergence stability.
UR - http://www.scopus.com/inward/record.url?scp=85098976030&partnerID=8YFLogxK
M3 - منشور من مؤتمر
T3 - ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
SP - 709
EP - 714
BT - ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
T2 - 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Y2 - 2 October 2020 through 4 October 2020
ER -