TY - GEN
T1 - A nested alignment graph kernel through the dynamic time warping framework
AU - Bai, Lu
AU - Rossi, Luca
AU - Cui, Lixin
AU - Hancock, Edwin R.
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (Grant no. 61503422 and 61602535), the Open Projects Program of National Laboratory of Pattern Recognition, the Young Scholar Development Fund of Central University of Finance and Economics (No. QJJ1540), and the program for innovation research in Central University of Finance and Economics.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017/5
Y1 - 2017/5
N2 - In this paper, we propose a novel nested alignment graph kernel drawing on depth-based complexity traces and the dynamic time warping framework. Specifically, for a pair of graphs, we commence by computing the depth-based complexity traces rooted at the centroid vertices. The resulting kernel for the graphs is defined by measuring the global alignment kernel, which is developed through the dynamic time warping framework, between the complexity traces. We show that the proposed kernel simultaneously considers the local and global graph characteristics in terms of the complexity traces, but also provides richer statistic measures by incorporating the whole spectrum of alignment costs between these traces. Our experiments demonstrate the effectiveness and efficiency of the proposed kernel.
AB - In this paper, we propose a novel nested alignment graph kernel drawing on depth-based complexity traces and the dynamic time warping framework. Specifically, for a pair of graphs, we commence by computing the depth-based complexity traces rooted at the centroid vertices. The resulting kernel for the graphs is defined by measuring the global alignment kernel, which is developed through the dynamic time warping framework, between the complexity traces. We show that the proposed kernel simultaneously considers the local and global graph characteristics in terms of the complexity traces, but also provides richer statistic measures by incorporating the whole spectrum of alignment costs between these traces. Our experiments demonstrate the effectiveness and efficiency of the proposed kernel.
UR - http://www.scopus.com/inward/record.url?scp=85019642275&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58961-9_6
DO - 10.1007/978-3-319-58961-9_6
M3 - Conference article published in proceeding or book
AN - SCOPUS:85019642275
SN - 9783319589602
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 59
EP - 69
BT - Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings
A2 - Foggia, Pasquale
A2 - Vento, Mario
A2 - Liu, Cheng-Lin
PB - Springer Verlag
T2 - 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017
Y2 - 16 May 2017 through 18 May 2017
ER -