Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding. Peluffo-Ordóñez, D., H., Lee, J., A., & Verleysen, M. Advances in Intelligent Systems and Computing, pages 65-74. 2014. ![Advances in Intelligent Systems and Computing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 2 downloads Dimensionality reduction methods aimed at preserving the data topology have shown to be suitable for reaching high-quality embedded data. In particular, those based on divergences such as stochastic neighbour embedding (SNE). The big advantage of SNE and its variants is that the neighbor preservation is done by optimizing the similarities in both high- and low-dimensional space. This work presents a brief review of SNE-based methods. Also, a comparative analysis of the considered methods is provided, which is done on important aspects such as algorithm implementation, relationship between methods, and performance. The aim of this paper is to investigate recent alternatives to SNE as well as to provide substantial results and discussion to compare them. © Springer International Publishing Switzerland 2014.
@inbook{
type = {inbook},
year = {2014},
keywords = {Dimensionality reduction,divergences,similarity,stochastic neighbor embedding},
pages = {65-74},
websites = {http://link.springer.com/10.1007/978-3-319-07695-9_6},
id = {b34262b6-4f5c-3d0e-998b-d361b58ffb1b},
created = {2022-01-26T03:01:04.376Z},
file_attached = {false},
profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
last_modified = {2022-01-26T03:01:04.376Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {Peluffo-Ordonez2014d},
private_publication = {false},
abstract = {Dimensionality reduction methods aimed at preserving the data topology have shown to be suitable for reaching high-quality embedded data. In particular, those based on divergences such as stochastic neighbour embedding (SNE). The big advantage of SNE and its variants is that the neighbor preservation is done by optimizing the similarities in both high- and low-dimensional space. This work presents a brief review of SNE-based methods. Also, a comparative analysis of the considered methods is provided, which is done on important aspects such as algorithm implementation, relationship between methods, and performance. The aim of this paper is to investigate recent alternatives to SNE as well as to provide substantial results and discussion to compare them. © Springer International Publishing Switzerland 2014.},
bibtype = {inbook},
author = {Peluffo-Ordóñez, Diego H. and Lee, John A. and Verleysen, Michel},
doi = {10.1007/978-3-319-07695-9_6},
chapter = {Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding},
title = {Advances in Intelligent Systems and Computing}
}
Downloads: 2
{"_id":"MEe8Be7mTzpLXHXbD","bibbaseid":"peluffoordez-lee-verleysen-advancesinintelligentsystemsandcomputing-2014","authorIDs":[],"author_short":["Peluffo-Ordóñez, D., H.","Lee, J., A.","Verleysen, M."],"bibdata":{"type":"inbook","year":"2014","keywords":"Dimensionality reduction,divergences,similarity,stochastic neighbor embedding","pages":"65-74","websites":"http://link.springer.com/10.1007/978-3-319-07695-9_6","id":"b34262b6-4f5c-3d0e-998b-d361b58ffb1b","created":"2022-01-26T03:01:04.376Z","file_attached":false,"profile_id":"aba9653c-d139-3f95-aad8-969c487ed2f3","group_id":"b9022d50-068c-31b4-9174-ebfaaf9ee57b","last_modified":"2022-01-26T03:01:04.376Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Peluffo-Ordonez2014d","private_publication":false,"abstract":"Dimensionality reduction methods aimed at preserving the data topology have shown to be suitable for reaching high-quality embedded data. In particular, those based on divergences such as stochastic neighbour embedding (SNE). The big advantage of SNE and its variants is that the neighbor preservation is done by optimizing the similarities in both high- and low-dimensional space. This work presents a brief review of SNE-based methods. Also, a comparative analysis of the considered methods is provided, which is done on important aspects such as algorithm implementation, relationship between methods, and performance. The aim of this paper is to investigate recent alternatives to SNE as well as to provide substantial results and discussion to compare them. © Springer International Publishing Switzerland 2014.","bibtype":"inbook","author":"Peluffo-Ordóñez, Diego H. and Lee, John A. and Verleysen, Michel","doi":"10.1007/978-3-319-07695-9_6","chapter":"Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding","title":"Advances in Intelligent Systems and Computing","bibtex":"@inbook{\n type = {inbook},\n year = {2014},\n keywords = {Dimensionality reduction,divergences,similarity,stochastic neighbor embedding},\n pages = {65-74},\n websites = {http://link.springer.com/10.1007/978-3-319-07695-9_6},\n id = {b34262b6-4f5c-3d0e-998b-d361b58ffb1b},\n created = {2022-01-26T03:01:04.376Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},\n last_modified = {2022-01-26T03:01:04.376Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Peluffo-Ordonez2014d},\n private_publication = {false},\n abstract = {Dimensionality reduction methods aimed at preserving the data topology have shown to be suitable for reaching high-quality embedded data. In particular, those based on divergences such as stochastic neighbour embedding (SNE). The big advantage of SNE and its variants is that the neighbor preservation is done by optimizing the similarities in both high- and low-dimensional space. This work presents a brief review of SNE-based methods. Also, a comparative analysis of the considered methods is provided, which is done on important aspects such as algorithm implementation, relationship between methods, and performance. The aim of this paper is to investigate recent alternatives to SNE as well as to provide substantial results and discussion to compare them. © Springer International Publishing Switzerland 2014.},\n bibtype = {inbook},\n author = {Peluffo-Ordóñez, Diego H. and Lee, John A. and Verleysen, Michel},\n doi = {10.1007/978-3-319-07695-9_6},\n chapter = {Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding},\n title = {Advances in Intelligent Systems and Computing}\n}","author_short":["Peluffo-Ordóñez, D., H.","Lee, J., A.","Verleysen, M."],"urls":{"Website":"http://link.springer.com/10.1007/978-3-319-07695-9_6"},"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","bibbaseid":"peluffoordez-lee-verleysen-advancesinintelligentsystemsandcomputing-2014","role":"author","keyword":["Dimensionality reduction","divergences","similarity","stochastic neighbor embedding"],"metadata":{"authorlinks":{}},"downloads":2},"bibtype":"inbook","creationDate":"2020-12-30T00:49:12.167Z","downloads":2,"keywords":["dimensionality reduction","divergences","similarity","stochastic neighbor embedding"],"search_terms":["advances","intelligent","systems","computing","peluffo-ordóñez","lee","verleysen"],"title":"Advances in Intelligent Systems and Computing","year":2014,"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","dataSources":["YEF3uFAbDNQXrkgNw","ya2CyA73rpZseyrZ8"]}