TY - GEN
T1 - Matrix factorization with interval-valued data
AU - Li, Mao Lin
AU - Di Mauro, Francesco
AU - Selcuk Candan, K.
AU - Sapino, Maria Luisa
N1 - Funding Information:
Partially funded by: NSF #1610282 (DataStorm), #1633381 (Complex Systems), #1629888 (GEARS), #1827757 (PFI-RP), and #1909555 (pCAR)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discovery and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.
AB - With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discovery and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.
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U2 - 10.1109/ICDE48307.2020.00240
DO - 10.1109/ICDE48307.2020.00240
M3 - Conference contribution
AN - SCOPUS:85085857855
T3 - Proceedings - International Conference on Data Engineering
SP - 2042
EP - 2043
BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PB - IEEE Computer Society
T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020
Y2 - 20 April 2020 through 24 April 2020
ER -