Please use this identifier to cite or link to this item:
|Title:||Learning with similarity functions : a tensor-based framework||Authors:||Ragusa, Edoardo
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Ragusa, 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/s12559-018-9590-9||Journal:||Cognitive Computation||Abstract:||Machine 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.||URI:||https://hdl.handle.net/10356/150714||ISSN:||1866-9956||DOI:||10.1007/s12559-018-9590-9||Rights:||© 2018 Springer Science Business Media, LLC, part of Springer Nature. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on Oct 23, 2021
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.