| G. Drakopoulos, E. Spyrou, Ph. Mylonas |
| Tensor Clustering: A Review |
| 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2019) |
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ABSTRACT
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| Tensor algebra is the next evolutionary step of linear algebra to more than two dimensions. Its plethora of applications include signal processing, big data, deep learning, multivariate numerical analysis, information retrieval, and social media analysis. As is precisely the case with data matrices, decompositions and factorizations with special properties reveal inherent but latent patterns which are not immediately discernible. Alternatively, for large tensors direct clustering can yield similar patterns. Once identified, said patterns can pave the way for other operations commonly found in a knowledge mining pipeline such as compression, outlier discovery, and higher order statistics. This survey concisely presents the key tensor clustering techniques as well as their applications. Additionally, deep learning frameworks which natively support tensors such as TensorFlow, Breeze, Spark MLlib, and Tensor Toolbox are presented.
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| 08 June , 2019 |
| G. Drakopoulos, E. Spyrou, Ph. Mylonas, "Tensor Clustering: A Review", 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2019) |
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