Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150714
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRagusa, Edoardoen_US
dc.contributor.authorGastaldo, Paoloen_US
dc.contributor.authorZunino, Rodolfoen_US
dc.contributor.authorCambria, Eriken_US
dc.date.accessioned2021-06-08T03:09:59Z-
dc.date.available2021-06-08T03:09:59Z-
dc.date.issued2019-
dc.identifier.citationRagusa, E., Gastaldo, P., Zunino, R. & Cambria, E. (2019). Learning with similarity functions : a tensor-based framework. Cognitive Computation, 11(1), 31-49. https://dx.doi.org/10.1007/s12559-018-9590-9en_US
dc.identifier.issn1866-9956en_US
dc.identifier.other0000-0002-3030-1280-
dc.identifier.urihttps://hdl.handle.net/10356/150714-
dc.description.abstractMachine learning algorithms are typically designed to deal with data represented as vectors. Several major applications, however, involve multi-way data, such as video sequences and multi-sensory arrays. In those cases, tensors endow a more consistent way to capture multi-modal relations, which may be lost by a conventional remapping of original data into a vector representation. This paper presents a tensor-oriented machine learning framework, and shows that the theory of learning with similarity functions provides an effective paradigm to support this framework. The proposed approach adopts a specific similarity function, which defines a measure of similarity between a pair of tensors. The performance of the tensor-based framework is evaluated on a set of complex, real-world, pattern-recognition problems. Experimental results confirm the effectiveness of the framework, which compares favorably with state-of-the-art machine learning methodologies that can accept tensors as inputs. Indeed, a formal analysis proves that the framework is more efficient than state-of-the-art methodologies also in terms of computational cost. The paper thus provides two main outcomes: (1) a theoretical framework that enables the use of tensor-oriented similarity notions and (2) a cognitively inspired notion of similarity that leads to computationally efficient predictors.en_US
dc.language.isoenen_US
dc.relation.ispartofCognitive Computationen_US
dc.rights© 2018 Springer Science Business Media, LLC, part of Springer Nature. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleLearning with similarity functions : a tensor-based frameworken_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1007/s12559-018-9590-9-
dc.identifier.scopus2-s2.0-85053384656-
dc.identifier.issue1en_US
dc.identifier.volume11en_US
dc.identifier.spage31en_US
dc.identifier.epage49en_US
dc.subject.keywordsTensor Dataen_US
dc.subject.keywordsSimilarity Functionsen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:SCSE Journal Articles

Page view(s)

27
Updated on Oct 23, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.