An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals
Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation. However finding appropriate models from high-dimensional data sampled from biosignals is in general unpracticable due to the problem known as the &...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9260133/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533405066428416 |
|---|---|
| author | Lorenzo Manoni Claudio Turchetti Laura Falaschetti |
| author_facet | Lorenzo Manoni Claudio Turchetti Laura Falaschetti |
| author_sort | Lorenzo Manoni |
| collection | DOAJ |
| description | Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation. However finding appropriate models from high-dimensional data sampled from biosignals is in general unpracticable due to the problem known as the “curse of dimensionality”. Dimensionality reduction, that is representing data in some lower-dimensional space, is the commonly adopted technique to handle these data. In this context <italic>manifold learning</italic> has drawn great interests as a promising nonlinear dimensionality reduction method. Neverthless the main drawback of methods based on manifold learning is that they learn data implicitly, that is with no explicit model of data belonging to the manifold. The aim of this article is to develop a manifold learning approach to parametrize data for generative modeling of biosignals, by deriving an explicit function that represents the local parametrization of the manifold. The approach involves two main stages, <italic>i)</italic> estimation of the intrinsic dimension of data, that is the dimension of the manifold, and <italic>ii)</italic> estimation of the function representing the local parametrization of the manifold. Experimental results both on synthetic and real-world data shown the effectiveness of the presented approach. The source code of the algorithm for unsupervised learning of data is available at <uri>https://codeocean.com/capsule/6692152/tree/v3</uri>. |
| format | Article |
| id | doaj-art-4d2b01bf3fed440480527266bdd03b17 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4d2b01bf3fed440480527266bdd03b172025-01-16T00:01:06ZengIEEEIEEE Access2169-35362020-01-01820711220713310.1109/ACCESS.2020.30383149260133An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of BiosignalsLorenzo Manoni0https://orcid.org/0000-0001-6996-6928Claudio Turchetti1https://orcid.org/0000-0001-8713-9790Laura Falaschetti2https://orcid.org/0000-0003-3183-7682Dipartimento di Ingegneria dell’Informazione (DII), Università Politecnica delle Marche, Ancona, ItalyDipartimento di Ingegneria dell’Informazione (DII), Università Politecnica delle Marche, Ancona, ItalyDipartimento di Ingegneria dell’Informazione (DII), Università Politecnica delle Marche, Ancona, ItalyModeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation. However finding appropriate models from high-dimensional data sampled from biosignals is in general unpracticable due to the problem known as the “curse of dimensionality”. Dimensionality reduction, that is representing data in some lower-dimensional space, is the commonly adopted technique to handle these data. In this context <italic>manifold learning</italic> has drawn great interests as a promising nonlinear dimensionality reduction method. Neverthless the main drawback of methods based on manifold learning is that they learn data implicitly, that is with no explicit model of data belonging to the manifold. The aim of this article is to develop a manifold learning approach to parametrize data for generative modeling of biosignals, by deriving an explicit function that represents the local parametrization of the manifold. The approach involves two main stages, <italic>i)</italic> estimation of the intrinsic dimension of data, that is the dimension of the manifold, and <italic>ii)</italic> estimation of the function representing the local parametrization of the manifold. Experimental results both on synthetic and real-world data shown the effectiveness of the presented approach. The source code of the algorithm for unsupervised learning of data is available at <uri>https://codeocean.com/capsule/6692152/tree/v3</uri>.https://ieeexplore.ieee.org/document/9260133/Biosignal generative modelingintrinsic dimensionlatent variablesmanifold learningnonlinear dynamical systemsregression |
| spellingShingle | Lorenzo Manoni Claudio Turchetti Laura Falaschetti An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals IEEE Access Biosignal generative modeling intrinsic dimension latent variables manifold learning nonlinear dynamical systems regression |
| title | An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals |
| title_full | An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals |
| title_fullStr | An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals |
| title_full_unstemmed | An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals |
| title_short | An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals |
| title_sort | effective manifold learning approach to parametrize data for generative modeling of biosignals |
| topic | Biosignal generative modeling intrinsic dimension latent variables manifold learning nonlinear dynamical systems regression |
| url | https://ieeexplore.ieee.org/document/9260133/ |
| work_keys_str_mv | AT lorenzomanoni aneffectivemanifoldlearningapproachtoparametrizedataforgenerativemodelingofbiosignals AT claudioturchetti aneffectivemanifoldlearningapproachtoparametrizedataforgenerativemodelingofbiosignals AT laurafalaschetti aneffectivemanifoldlearningapproachtoparametrizedataforgenerativemodelingofbiosignals AT lorenzomanoni effectivemanifoldlearningapproachtoparametrizedataforgenerativemodelingofbiosignals AT claudioturchetti effectivemanifoldlearningapproachtoparametrizedataforgenerativemodelingofbiosignals AT laurafalaschetti effectivemanifoldlearningapproachtoparametrizedataforgenerativemodelingofbiosignals |