@inproceedings{dd510bcb8ea24f2f8e9332201e111937,
title = "A multi-scale approach for data imputation",
abstract = "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.",
author = "Neta Rabin and Dalia Fishelov",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 ; Conference date: 12-12-2018 Through 14-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICSEE.2018.8646284",
language = "الإنجليزيّة",
series = "2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018",
address = "الولايات المتّحدة",
}