Nonnegative Coupled Matrix Tensor Factorization for Smart City Spatiotemporal Pattern Mining

IEEE Trans. Knowl. Data Eng.

Yuen

et al. 2021

Self Cite

Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors.

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Section: Related Workmentioning

confidence: 99%

“…where X 1 is the mode-1 matricization of the tensor. (11) can be rewritten using (12) and (13) as follows: (2) . (14) For simplicity, each element of the gradient G is represented as:…”

Section: Gradient Calculationmentioning

confidence: 99%

Section: Experimental Setup and Benchmarksmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Column-Wise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization

IEEE Trans. Knowl. Data Eng.

Yuen

et al. 2021

Self Cite

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“…Secondly, the short texts from social media like Twitter can induce sparseness to the tensor representation. The state-of-the-art factorization algorithms may fail to effectively learn the spatio-temporal patterns in the presence of noise and sparsity present in social media data [7], [8]. Finally, the larger data size introduces efficiency issues in factorization process [9].…”

mentioning

confidence: 99%

Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

Bashar

2020

Self Cite

Traditional topic modelling methods such as Latent Dirichlet Allocation (LDA) [2] are challenged with short text data and face sparsity and low word co-occurrence issues. Factorization methods like Non-negative Matrix Factorization (NMF) [3] map the high-dimensional (sparse) text representation to a lower-dimensional representation. These methods have become popular in text mining due to their capability to capture the patterns in the lower dimensional representation of the data [4]. NMF decomposes a high-dimensional (tweet × term) matrix into two low-rank factor matrices that represent tweet and term clusters. It produces a part-based representation by allowing only additive combinations of basis components [4].Non-negative Tensor Factorization (NTF) [5], an extension of NMF for high-dimensional data, can identify associations among multiple dimensions [6]. This brings an added advantage to NTF over NMF as the patterns can be interpreted with associations. NTF based methods have been used for spatiotemporal patterns elicitation on traditional (i.e. structured) data. NTF for spatio-temporal pattern elicitation on social media (i.e. unstructured) data brings multiple challenges. Firstly, representing this unstructured text data and spatiotemporal information in a single tensor representation model is challenging when there is a need to preserve the association among them. Secondly, the short texts from social media like Twitter can induce sparseness to the tensor representation. The state-of-the-art factorization algorithms may fail to effectively learn the spatio-temporal patterns in the presence of noise and sparsity present in social media data [7], [8]. Finally, the larger data size introduces efficiency issues in factorization process [9]. Therefore, a proper data representation and selection of a suitable factorization algorithm is crucial to deal with social media data.In this paper, we present a novel NTF based spatio-temporal topic dynamics discovery method. We focus on applying the best-suited data representation model and the factorization algorithm to understand the spatio-temporal distribution of topics emerging from users' interactions on social media related to Covid-19. We present a case study analysing a large tweet Abstract-Social media platforms facilitate mankind a datadriven world by enabling billions of people to share their thoughts and activities ubiquitously. This huge collection of data, if analysed properly, can provide useful insights into people's behavior. More than ever, now is a crucial time under the Covid-19 pandemic to understand people's online behaviors detailing what topics are being discussed, and where (space) and when (time) they are discussed. Given the high complexity and poor quality of the huge social media data, an effective spatio-temporal topic detection method is needed. This paper proposes a tensor-based representation of social media data and Non-negative Tensor Factorization (NTF) to identify the topics discussed in social media data along with the spatio-tempora...

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Sparsity Constraint Nonnegative Tensor Factorization for Mobility Pattern Mining

PRICAI 2019: Trends in Artificial Intelligence

Yuen

2019