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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.06831 |
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| _version_ | 1866929496157847552 |
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| author | Băltoiu, Andra Ilie-Ablachim, Denis C. Dumitrescu, Bogdan |
| author_facet | Băltoiu, Andra Ilie-Ablachim, Denis C. Dumitrescu, Bogdan |
| contents | Recent work on dictionary learning with set-atoms has shown benefits in anomaly detection. Instead of viewing an atom as a single vector, these methods allow building sparse representations with atoms taken from a set around a central vector; the set can be a cone or may have a probability distribution associated to it. We propose a method for adaptively adjusting the size of set-atoms in Gaussian and cone dictionary learning. The purpose of the algorithm is to match the atom sizes with their contribution in representing the signals. The proposed algorithm not only decreases the representation error, but also improves anomaly detection, for a class of anomalies called `dependency'. We obtain better detection performance than state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06831 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Atom dimension adaptation for infinite set dictionary learning Băltoiu, Andra Ilie-Ablachim, Denis C. Dumitrescu, Bogdan Machine Learning 65K10 I.2.6; I.2.1 Recent work on dictionary learning with set-atoms has shown benefits in anomaly detection. Instead of viewing an atom as a single vector, these methods allow building sparse representations with atoms taken from a set around a central vector; the set can be a cone or may have a probability distribution associated to it. We propose a method for adaptively adjusting the size of set-atoms in Gaussian and cone dictionary learning. The purpose of the algorithm is to match the atom sizes with their contribution in representing the signals. The proposed algorithm not only decreases the representation error, but also improves anomaly detection, for a class of anomalies called `dependency'. We obtain better detection performance than state-of-the-art methods. |
| title | Atom dimension adaptation for infinite set dictionary learning |
| topic | Machine Learning 65K10 I.2.6; I.2.1 |
| url | https://arxiv.org/abs/2409.06831 |