Metrics Space and Norm: Taxonomy to Distance Metrics
A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand the data pattern well and to make the right decision based on the data. In recent years, studies show that distance metrics can significantly impro...
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ftdoajarticles:oai:doaj.org/article:3889d72ad42b4815b0c86b83773ee22a 2023-05-15T16:01:58+02:00 Metrics Space and Norm: Taxonomy to Distance Metrics Barathi Subramanian Anand Paul Jeonghong Kim K.-W.-A. Chee 2022-01-01T00:00:00Z https://doi.org/10.1155/2022/1911345 https://doaj.org/article/3889d72ad42b4815b0c86b83773ee22a EN eng Hindawi Limited http://dx.doi.org/10.1155/2022/1911345 https://doaj.org/toc/1875-919X 1875-919X doi:10.1155/2022/1911345 https://doaj.org/article/3889d72ad42b4815b0c86b83773ee22a Scientific Programming, Vol 2022 (2022) Computer software QA76.75-76.765 article 2022 ftdoajarticles https://doi.org/10.1155/2022/1911345 2022-12-30T23:29:25Z A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand the data pattern well and to make the right decision based on the data. In recent years, studies show that distance metrics can significantly improve the performance of the machine learning or deep learning model in clustering, classification, data recovery tasks, etc. In this article, we provide a survey on widely used distance metrics and the challenges associated with this field. The most current studies conducted in this area are commonly influenced by Siamese and triplet networks utilized to make associations between samples while employing mutual weights in deep metric learning (DML). They are successful because of their ability to recognize the relationships among samples that show a similarity. Furthermore, the sampling strategy, suitable distance metric, and network structure are complex and difficult factors for researchers to improve network model performance. So, this article is significant because it is the most recent detailed survey in which these components are comprehensively examined and valued as a whole, evidenced by assessing the numerical findings of the techniques. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles Scientific Programming 2022 1 11 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
Computer software QA76.75-76.765 |
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Computer software QA76.75-76.765 Barathi Subramanian Anand Paul Jeonghong Kim K.-W.-A. Chee Metrics Space and Norm: Taxonomy to Distance Metrics |
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Computer software QA76.75-76.765 |
description |
A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand the data pattern well and to make the right decision based on the data. In recent years, studies show that distance metrics can significantly improve the performance of the machine learning or deep learning model in clustering, classification, data recovery tasks, etc. In this article, we provide a survey on widely used distance metrics and the challenges associated with this field. The most current studies conducted in this area are commonly influenced by Siamese and triplet networks utilized to make associations between samples while employing mutual weights in deep metric learning (DML). They are successful because of their ability to recognize the relationships among samples that show a similarity. Furthermore, the sampling strategy, suitable distance metric, and network structure are complex and difficult factors for researchers to improve network model performance. So, this article is significant because it is the most recent detailed survey in which these components are comprehensively examined and valued as a whole, evidenced by assessing the numerical findings of the techniques. |
format |
Article in Journal/Newspaper |
author |
Barathi Subramanian Anand Paul Jeonghong Kim K.-W.-A. Chee |
author_facet |
Barathi Subramanian Anand Paul Jeonghong Kim K.-W.-A. Chee |
author_sort |
Barathi Subramanian |
title |
Metrics Space and Norm: Taxonomy to Distance Metrics |
title_short |
Metrics Space and Norm: Taxonomy to Distance Metrics |
title_full |
Metrics Space and Norm: Taxonomy to Distance Metrics |
title_fullStr |
Metrics Space and Norm: Taxonomy to Distance Metrics |
title_full_unstemmed |
Metrics Space and Norm: Taxonomy to Distance Metrics |
title_sort |
metrics space and norm: taxonomy to distance metrics |
publisher |
Hindawi Limited |
publishDate |
2022 |
url |
https://doi.org/10.1155/2022/1911345 https://doaj.org/article/3889d72ad42b4815b0c86b83773ee22a |
genre |
DML |
genre_facet |
DML |
op_source |
Scientific Programming, Vol 2022 (2022) |
op_relation |
http://dx.doi.org/10.1155/2022/1911345 https://doaj.org/toc/1875-919X 1875-919X doi:10.1155/2022/1911345 https://doaj.org/article/3889d72ad42b4815b0c86b83773ee22a |
op_doi |
https://doi.org/10.1155/2022/1911345 |
container_title |
Scientific Programming |
container_volume |
2022 |
container_start_page |
1 |
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11 |
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1766397628547858432 |