dc.contributor.author | Mikalsen, Karl Øyvind | |
dc.contributor.author | Bianchi, Filippo Maria | |
dc.contributor.author | Soguero-Ruiz, Cristina | |
dc.contributor.author | Jenssen, Robert | |
dc.date.accessioned | 2018-08-23T14:00:53Z | |
dc.date.available | 2018-08-23T14:00:53Z | |
dc.date.issued | 2017-12-06 | |
dc.description.abstract | <p>Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust time series cluster kernel (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel.</p>
<p>We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data. | en_US |
dc.description.sponsorship | The Spanish Government | en_US |
dc.description | Accepted manuscript version. Published version available at <a href=https://doi.org/10.1016/j.patcog.2017.11.030> https://doi.org/10.1016/j.patcog.2017.11.030</a>. Accepted manuscript version, licensed <a href=http://creativecommons.org/licenses/by-nc-nd/4.0/> CC BY-NC-ND 4.0.</a> | en_US |
dc.identifier.citation | Mikalsen, K.Ø., Bianchi, F.M., Soguero-Ruiz, C. & Jenssen, R. (2017). Time series cluster kernel for learning similarities between multivariate time series with missing data. <i>Pattern Recognition</i>, 76, 569-581. https://doi.org/10.1016/j.patcog.2017.11.030 | en_US |
dc.identifier.cristinID | FRIDAID 1537526 | |
dc.identifier.doi | https://doi.org/10.1016/j.patcog.2017.11.030 | |
dc.identifier.issn | 0031-3203 | |
dc.identifier.issn | 1873-5142 | |
dc.identifier.uri | https://hdl.handle.net/10037/13578 | |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | The final version of this article is part of<p>
Mikalsen, K.Ø. (2019). Advancing Unsupervised and Weakly Supervised Learning with Emphasis on Data-Driven Healthcare. (Doctoral thesis). <a href=http://hdl.handle.net/10037/14659>http://hdl.handle.net/10037/14659. </a> | |
dc.relation.journal | Pattern Recognition | |
dc.relation.projectID | info:eu-repo/grantAgreement/RCN/FRIPRO/239844/Norway/Next Generation Learning Machines// | en_US |
dc.relation.uri | https://www.sciencedirect.com/science/article/pii/S0031320317304843 | |
dc.rights.accessRights | openAccess | en_US |
dc.subject | VDP::Matematikk og naturvitenskap: 400::Matematikk: 410::Statistikk: 412 | en_US |
dc.subject | VDP::Mathematics and natural scienses: 400::Mathematics: 410::Statistics: 412 | en_US |
dc.subject | Maskinlæring | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Multivariate time series | en_US |
dc.subject | Similarity measures | en_US |
dc.subject | Kernel methods | en_US |
dc.subject | Missing data | en_US |
dc.subject | Gaussian mixture models | en_US |
dc.subject | Ensemble learning | en_US |
dc.title | Time series cluster kernel for learning similarities between multivariate time series with missing data | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |