TY - GEN
T1 - Exact hybrid covariance thresholding for joint graphical lasso
AU - Tang, Qingming
AU - Yang, Chao
AU - Peng, Jian
AU - Xu, Jinbo
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper studies precision matrix estimation for multiple related Gaussian graphical models from a dataset consisting of different classes, based upon the formulation of this problem as group graphical lasso. In particular, this paper proposes a novel hybrid covariance thresholding algorithm that can effectively identify zero entries in the precision matrices and split a large joint graphical lasso problem into many small subproblems. Our hybrid covariance thresholding method is superior to existing uniform thresholding methods in that our method can split the precision matrix of each individual class using different partition schemes and thus, split group graphical lasso into much smaller subproblems, each of which can be solved very fast. This paper also establishes necessary and sufficient conditions for our hybrid covariance thresholding algorithm. Experimental results on both synthetic and real data validate the superior performance of our thresholding method over the others.
AB - This paper studies precision matrix estimation for multiple related Gaussian graphical models from a dataset consisting of different classes, based upon the formulation of this problem as group graphical lasso. In particular, this paper proposes a novel hybrid covariance thresholding algorithm that can effectively identify zero entries in the precision matrices and split a large joint graphical lasso problem into many small subproblems. Our hybrid covariance thresholding method is superior to existing uniform thresholding methods in that our method can split the precision matrix of each individual class using different partition schemes and thus, split group graphical lasso into much smaller subproblems, each of which can be solved very fast. This paper also establishes necessary and sufficient conditions for our hybrid covariance thresholding algorithm. Experimental results on both synthetic and real data validate the superior performance of our thresholding method over the others.
UR - http://www.scopus.com/inward/record.url?scp=84959423089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959423089&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23525-7_36
DO - 10.1007/978-3-319-23525-7_36
M3 - Conference contribution
AN - SCOPUS:84959423089
SN - 9783319235240
SN - 9783319235240
SN - 9783319235240
SN - 9783319235240
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 593
EP - 607
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015
A2 - Costa, Vitor Santos
A2 - Soares, Carlos
A2 - Appice, Annalisa
A2 - Appice, Annalisa
A2 - Rodrigues, Pedro Pereira
A2 - Costa, Vitor Santos
A2 - Soares, Carlos
A2 - Gama, João
A2 - Jorge, Alípio
A2 - Rodrigues, Pedro Pereira
A2 - Gama, João
A2 - Costa, Vitor Santos
A2 - Jorge, Alípio
A2 - Appice, Annalisa
A2 - Rodrigues, Pedro Pereira
A2 - Gama, João
A2 - Appice, Annalisa
A2 - Soares, Carlos
A2 - Jorge, Alípio
A2 - Gama, João
A2 - Rodrigues, Pedro Pereira
A2 - Costa, Vitor Santos
A2 - Soares, Carlos
A2 - Jorge, Alípio
PB - Springer
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015
Y2 - 7 September 2015 through 11 September 2015
ER -