TY - GEN
T1 - Learning Dependence Representations with CNNs
AU - Kim, Taejun
AU - Kim, Han Joon
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - To measure statistical dependence, correlations have been widely used in the fields of statistics and machine learning. However, due to a large amount of types of nonlinear dependence, it is very challenging to distinguish the types with a correlation coefficient. In this paper, we present a new approach to capturing the dependence from 2D histograms, which has a potential to learn task-specific representations of dependence. With the representations learned on 427 datasets, our models are able to predict Pearson's correlation coefficient and distance correlation coefficient almost perfectly. Furthermore, in terms of computing speed of distance correlation, our proposed method is faster than Huo's method [1], when a sample size is large.
AB - To measure statistical dependence, correlations have been widely used in the fields of statistics and machine learning. However, due to a large amount of types of nonlinear dependence, it is very challenging to distinguish the types with a correlation coefficient. In this paper, we present a new approach to capturing the dependence from 2D histograms, which has a potential to learn task-specific representations of dependence. With the representations learned on 427 datasets, our models are able to predict Pearson's correlation coefficient and distance correlation coefficient almost perfectly. Furthermore, in terms of computing speed of distance correlation, our proposed method is faster than Huo's method [1], when a sample size is large.
KW - Convolutional Neural Networks
KW - Distance Correlation
KW - Learning Representations
KW - Statistical Dependence
UR - http://www.scopus.com/inward/record.url?scp=85062801887&partnerID=8YFLogxK
U2 - 10.1109/ICBDAA.2018.8629670
DO - 10.1109/ICBDAA.2018.8629670
M3 - Conference contribution
AN - SCOPUS:85062801887
T3 - 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
SP - 87
EP - 92
BT - 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
Y2 - 21 November 2018 through 22 November 2018
ER -