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dc.contributor.authorRagusa, Edoardoen_US
dc.contributor.authorGastaldo, Paoloen_US
dc.contributor.authorZunino, Rodolfoen_US
dc.contributor.authorCambria, Eriken_US
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.
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.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.subject.keywordsTensor Dataen_US
dc.subject.keywordsSimilarity Functionsen_US
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