TY - GEN
T1 - A multi-scale approach for data imputation
AU - Rabin, Neta
AU - Fishelov, Dalia
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - A common pre-possessing task in machine learning is to complete missing data entries in order to form a full dataset. In case the dimension of the input data is high, it is often the case that the rows and columns are correlated. In this work, we construct a multi-scale model that is based on the the dual row-column geometry of the dataset and apply it to imputation, which is carried out within the model construction. Experimental results demonstrate the efficiency of our approach on a publicly available dataset.
AB - A common pre-possessing task in machine learning is to complete missing data entries in order to form a full dataset. In case the dimension of the input data is high, it is often the case that the rows and columns are correlated. In this work, we construct a multi-scale model that is based on the the dual row-column geometry of the dataset and apply it to imputation, which is carried out within the model construction. Experimental results demonstrate the efficiency of our approach on a publicly available dataset.
UR - https://www.scopus.com/pages/publications/85063152500
U2 - 10.1109/ICSEE.2018.8646284
DO - 10.1109/ICSEE.2018.8646284
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AN - SCOPUS:85063152500
T3 - 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
BT - 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Y2 - 12 December 2018 through 14 December 2018
ER -