LLT: An R package for Linear Law-based Feature Space Transformation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911422751965184 |
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| author | Kurbucz, Marcell T. Pósfay, Péter Jakovác, Antal |
| author_facet | Kurbucz, Marcell T. Pósfay, Péter Jakovác, Antal |
| contents | The goal of the linear law-based feature space transformation (LLT) algorithm is to assist with the classification of univariate and multivariate time series. The presented R package, called LLT, implements this algorithm in a flexible yet user-friendly way. This package first splits the instances into training and test sets. It then utilizes time-delay embedding and spectral decomposition techniques to identify the governing patterns (called linear laws) of each input sequence (initial feature) within the training set. Finally, it applies the linear laws of the training set to transform the initial features of the test set. These steps are performed by three separate functions called trainTest, trainLaw, and testTrans. Their application requires a predefined data structure; however, for fast calculation, they use only built-in functions. The LLT R package and a sample dataset with the appropriate data structure are publicly available on GitHub. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2304_14211 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LLT: An R package for Linear Law-based Feature Space Transformation Kurbucz, Marcell T. Pósfay, Péter Jakovác, Antal Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Mathematical Software 62H30, 68T10, 62M10, 60-04 I.5; G.3; J.0; I.2.0 The goal of the linear law-based feature space transformation (LLT) algorithm is to assist with the classification of univariate and multivariate time series. The presented R package, called LLT, implements this algorithm in a flexible yet user-friendly way. This package first splits the instances into training and test sets. It then utilizes time-delay embedding and spectral decomposition techniques to identify the governing patterns (called linear laws) of each input sequence (initial feature) within the training set. Finally, it applies the linear laws of the training set to transform the initial features of the test set. These steps are performed by three separate functions called trainTest, trainLaw, and testTrans. Their application requires a predefined data structure; however, for fast calculation, they use only built-in functions. The LLT R package and a sample dataset with the appropriate data structure are publicly available on GitHub. |
| title | LLT: An R package for Linear Law-based Feature Space Transformation |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Mathematical Software 62H30, 68T10, 62M10, 60-04 I.5; G.3; J.0; I.2.0 |
| url | https://arxiv.org/abs/2304.14211 |