Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107201
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Jiang, Y | - |
dc.creator | Leung, FHF | - |
dc.date.accessioned | 2024-06-13T01:04:33Z | - |
dc.date.available | 2024-06-13T01:04:33Z | - |
dc.identifier.isbn | 978-1-5090-6014-6 (Electronic) | - |
dc.identifier.isbn | 978-1-5090-6015-3 (Print on Demand(PoD)) | - |
dc.identifier.uri | http://hdl.handle.net/10397/107201 | - |
dc.description | 2018 International Joint Conference on Neural Networks (IJCNN), 08-13 July 2018, Rio de Janeiro, Brazil | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Y. Jiang and H. F. Frank Leung, "The Scalable Version of Probabilistic Linear Discriminant Analysis and Its Potential as A Classifier for Audio Signal Classification," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018 is available at https://doi.org/10.1109/IJCNN.2018.8488995. | en_US |
dc.subject | Audio signal classification | en_US |
dc.subject | Probabilistic linear discriminant analysis | en_US |
dc.title | The scalable version of probabilistic linear discriminant analysis and its potential as a classifier for audio signal classification | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | 10.1109/IJCNN.2018.8488995 | - |
dcterms.abstract | Probabilistic Linear Discriminant Analysis (PLDA) has exhibited good performance in face recognition and speaker verification. However, it is not widely used as a general-purpose classifier. The major limitation of PLDA lies in that, in the original formulation, the modeling part and the prediction part require the inversion of large matrices, whose sizes are proportional to the number of training vectors in a class. The original formulation of PLDA is not scalable if there are many training vectors, because the matrices will become too large to be inverted. In the literature, some scalable versions for the modeling part have been proposed. In this paper, we propose the scalable version for the prediction part, which completes the scalable version of PLDA. This makes PLDA able to handle a large number of training data, enabling PLDA to be used as a general-purpose classifier for different classification tasks. We then apply PLDA as the classifier to three different audio signal classification tasks, and compare its performance with Support Vector Machine (SVM), which is a widely used general-purpose classifier. Experimental results show that PLDA performs very well and can be even better than SVM, in terms of classification accuracy. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of 2018 International Joint Conference on Neural Networks (IJCNN), 08-13 July 2018, Rio de Janeiro, Brazil | - |
dcterms.issued | 2018 | - |
dc.identifier.scopus | 2-s2.0-85056529202 | - |
dc.relation.conference | International Joint Conference on Neural Networks [IJCNN] | - |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0497 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20096073 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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Jiang_Scalable_Version_Probabilistic.pdf | Pre-Published version | 749.14 kB | Adobe PDF | View/Open |
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