TY - GEN
T1 - Structurally Regularized Non-negative Tensor Factorization for Spatio-Temporal Pattern Discoveries
AU - Takeuchi, Koh
AU - Kawahara, Yoshinobu
AU - Iwata, Tomoharu
N1 - Funding Information:
Acknowledgements. The part of this work was supported by JSPS KAKENHI Grant Numbers JP16H01548 and JP26280086, and NICT “Research and Development on Fundamental and Utilization Technologies for Social Big Data”.
PY - 2017
Y1 - 2017
N2 - Understanding spatio-temporal activities in a city is a typical problem of spatio-temporal data analysis. For this analysis, tensor factorization methods have been widely applied for extracting a few essential patterns into latent factors. Non-negative Tensor Factorization (NTF) is popular because of its capability of learning interpretable factors from non-negative data, simple computation procedures, and dealing with missing observation. However, since existing NTF methods are not fully aware of spatial and temporal dependencies, they often fall short of learning latent factors where a large portion of missing observation exist in data. In this paper, we present a novel NTF method for extracting smooth and flat latent factors by leveraging various kinds of spatial and temporal structures. Our method incorporates a unified structured regularizer into NTF that can represent various kinds of auxiliary information, such as an order of timestamps, a daily and weekly periodicity, distances between sensor locations, and areas of locations. For the estimation of the factors for our model, we present a simple and efficient optimization procedure based on the alternating direction method of multipliers. In missing value interpolation experiments of traffic flow data and bike-sharing system data, we demonstrate that our proposed method improved interpolation performances from existing NTF, especially when a large portion of missing values exists.
AB - Understanding spatio-temporal activities in a city is a typical problem of spatio-temporal data analysis. For this analysis, tensor factorization methods have been widely applied for extracting a few essential patterns into latent factors. Non-negative Tensor Factorization (NTF) is popular because of its capability of learning interpretable factors from non-negative data, simple computation procedures, and dealing with missing observation. However, since existing NTF methods are not fully aware of spatial and temporal dependencies, they often fall short of learning latent factors where a large portion of missing observation exist in data. In this paper, we present a novel NTF method for extracting smooth and flat latent factors by leveraging various kinds of spatial and temporal structures. Our method incorporates a unified structured regularizer into NTF that can represent various kinds of auxiliary information, such as an order of timestamps, a daily and weekly periodicity, distances between sensor locations, and areas of locations. For the estimation of the factors for our model, we present a simple and efficient optimization procedure based on the alternating direction method of multipliers. In missing value interpolation experiments of traffic flow data and bike-sharing system data, we demonstrate that our proposed method improved interpolation performances from existing NTF, especially when a large portion of missing values exists.
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U2 - 10.1007/978-3-319-71249-9_35
DO - 10.1007/978-3-319-71249-9_35
M3 - Conference contribution
AN - SCOPUS:85040238967
SN - 9783319712482
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 582
EP - 598
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
A2 - Ceci, Michelangelo
A2 - Dzeroski, Saso
A2 - Vens, Celine
A2 - Todorovski, Ljupco
A2 - Hollmen, Jaakko
PB - Springer Verlag
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
Y2 - 18 September 2017 through 22 September 2017
ER -