Adaptive granularity in tensors: A quest for interpretable structure
Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus, the utility of such tensors is low, in terms of the amount of interpretable and exploitable structure that one can extract from them. In thi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2022.929511/full |
| _version_ | 1811215967374540800 |
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| author | Ravdeep S. Pasricha Ekta Gujral Evangelos E. Papalexakis |
| author_facet | Ravdeep S. Pasricha Ekta Gujral Evangelos E. Papalexakis |
| author_sort | Ravdeep S. Pasricha |
| collection | DOAJ |
| description | Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus, the utility of such tensors is low, in terms of the amount of interpretable and exploitable structure that one can extract from them. In this paper, we introduce the problem of finding a tensor of adaptive aggregated granularity that can be decomposed to reveal meaningful latent concepts (structures) from datasets that, in their original form, are not amenable to tensor analysis. Such datasets fall under the broad category of sparse point processes that evolve over space and/or time. To the best of our knowledge, this is the first work that explores adaptive granularity aggregation in tensors. Furthermore, we formally define the problem and discuss different definitions of “good structure” that are in practice and show that the optimal solution is of prohibitive combinatorial complexity. Subsequently, we propose an efficient and effective greedy algorithm called ICEBREAKER, which follows a number of intuitive decision criteria that locally maximize the “goodness of structure,” resulting in high-quality tensors. We evaluate our method on synthetic, semi-synthetic, and real datasets. In all the cases, our proposed method constructs tensors that have a very high structure quality. |
| first_indexed | 2024-04-12T06:31:14Z |
| format | Article |
| id | doaj.art-395d31e6c9db4c95a3a5fc02a7b0184c |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-04-12T06:31:14Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-395d31e6c9db4c95a3a5fc02a7b0184c2022-12-22T03:44:00ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2022-11-01510.3389/fdata.2022.929511929511Adaptive granularity in tensors: A quest for interpretable structureRavdeep S. PasrichaEkta GujralEvangelos E. PapalexakisData collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus, the utility of such tensors is low, in terms of the amount of interpretable and exploitable structure that one can extract from them. In this paper, we introduce the problem of finding a tensor of adaptive aggregated granularity that can be decomposed to reveal meaningful latent concepts (structures) from datasets that, in their original form, are not amenable to tensor analysis. Such datasets fall under the broad category of sparse point processes that evolve over space and/or time. To the best of our knowledge, this is the first work that explores adaptive granularity aggregation in tensors. Furthermore, we formally define the problem and discuss different definitions of “good structure” that are in practice and show that the optimal solution is of prohibitive combinatorial complexity. Subsequently, we propose an efficient and effective greedy algorithm called ICEBREAKER, which follows a number of intuitive decision criteria that locally maximize the “goodness of structure,” resulting in high-quality tensors. We evaluate our method on synthetic, semi-synthetic, and real datasets. In all the cases, our proposed method constructs tensors that have a very high structure quality.https://www.frontiersin.org/articles/10.3389/fdata.2022.929511/fulltensorunsupervised learningtemporal granularitytensor decompositionmulti-aspect data |
| spellingShingle | Ravdeep S. Pasricha Ekta Gujral Evangelos E. Papalexakis Adaptive granularity in tensors: A quest for interpretable structure Frontiers in Big Data tensor unsupervised learning temporal granularity tensor decomposition multi-aspect data |
| title | Adaptive granularity in tensors: A quest for interpretable structure |
| title_full | Adaptive granularity in tensors: A quest for interpretable structure |
| title_fullStr | Adaptive granularity in tensors: A quest for interpretable structure |
| title_full_unstemmed | Adaptive granularity in tensors: A quest for interpretable structure |
| title_short | Adaptive granularity in tensors: A quest for interpretable structure |
| title_sort | adaptive granularity in tensors a quest for interpretable structure |
| topic | tensor unsupervised learning temporal granularity tensor decomposition multi-aspect data |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2022.929511/full |
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