TY - GEN
T1 - Latent Pattern Identification Using Orthogonal-Constraint Coupled Nonnegative Matrix Factorization
AU - Balasubramaniam, Anandkumar
AU - Balasubramaniam, Thirunavukarasu
AU - Paul, Anand
AU - Nayak, Richi
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The recent advancements and developments in Intelligent Transportation Systems (ITS) lead to the generation of abundant spatio-temporal traffic data. Identifying or understanding the latent patterns present in these spatio-temporal traffic data is very much essential and also challenging due to the fact that there is a chance of obtaining duplicate or similar patterns during the process of common pattern identification. This paper proposes an Orthogonal-Constraint Coupled Nonnegative Matrix Factorization (OC-CNMF) method and studies how to effectively identify the common as well as distinctive patterns that are hidden in the spatio-temporal traffic-related datasets. The distinctiveness of the patterns is achieved by the imposition of the orthogonality constraint in CNMF during the process of factorization. The imposition of the orthogonal constraint helps to ignore similar/duplicate patterns among the identification of the common patterns. We have shown that imposing orthogonality constraint in CNMF improves the convergence performance of the model and is able to identify common as well as distinctive patterns. Also, the performance of the OC-CNMF model is evaluated by comparing it with various performance evaluation measures.
AB - The recent advancements and developments in Intelligent Transportation Systems (ITS) lead to the generation of abundant spatio-temporal traffic data. Identifying or understanding the latent patterns present in these spatio-temporal traffic data is very much essential and also challenging due to the fact that there is a chance of obtaining duplicate or similar patterns during the process of common pattern identification. This paper proposes an Orthogonal-Constraint Coupled Nonnegative Matrix Factorization (OC-CNMF) method and studies how to effectively identify the common as well as distinctive patterns that are hidden in the spatio-temporal traffic-related datasets. The distinctiveness of the patterns is achieved by the imposition of the orthogonality constraint in CNMF during the process of factorization. The imposition of the orthogonal constraint helps to ignore similar/duplicate patterns among the identification of the common patterns. We have shown that imposing orthogonality constraint in CNMF improves the convergence performance of the model and is able to identify common as well as distinctive patterns. Also, the performance of the OC-CNMF model is evaluated by comparing it with various performance evaluation measures.
KW - Coupled nonnegative matrix factorization
KW - Orthogonal constraint
KW - Spatiotemporal
KW - Vehicular traffic pattern mining
UR - http://www.scopus.com/inward/record.url?scp=85144828637&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-22695-3_47
DO - 10.1007/978-3-031-22695-3_47
M3 - Conference contribution
AN - SCOPUS:85144828637
SN - 9783031226946
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 675
EP - 689
BT - AI 2022
A2 - Aziz, Haris
A2 - Corrêa, Débora
A2 - French, Tim
PB - Springer Science and Business Media Deutschland GmbH
T2 - 35th Australasian Joint Conference on Artificial Intelligence, AI 2022
Y2 - 5 December 2022 through 9 December 2022
ER -