Fast Dual-Regularized Autoencoder for Sparse Biological Data
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909135351578624 |
|---|---|
| author | Poleksic, Aleksandar |
| author_facet | Poleksic, Aleksandar |
| contents | Relationship inference from sparse data is an important task with applications ranging from product recommendation to drug discovery. A recently proposed linear model for sparse matrix completion has demonstrated surprising advantage in speed and accuracy over more sophisticated recommender systems algorithms. Here we extend the linear model to develop a shallow autoencoder for the dual neighborhood-regularized matrix completion problem. We demonstrate the speed and accuracy advantage of our approach over the existing state-of-the-art in predicting drug-target interactions and drug-disease associations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16664 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Dual-Regularized Autoencoder for Sparse Biological Data Poleksic, Aleksandar Machine Learning 92C42 J.3 Relationship inference from sparse data is an important task with applications ranging from product recommendation to drug discovery. A recently proposed linear model for sparse matrix completion has demonstrated surprising advantage in speed and accuracy over more sophisticated recommender systems algorithms. Here we extend the linear model to develop a shallow autoencoder for the dual neighborhood-regularized matrix completion problem. We demonstrate the speed and accuracy advantage of our approach over the existing state-of-the-art in predicting drug-target interactions and drug-disease associations. |
| title | Fast Dual-Regularized Autoencoder for Sparse Biological Data |
| topic | Machine Learning 92C42 J.3 |
| url | https://arxiv.org/abs/2401.16664 |