Robust nonparametric probability density estimation by soft clustering

Neural networks
Authors
Ezequiel López-Rubio , Juan Miguel Ortiz-De-Lazcano-Lobato , Domingo López-Rodríguez , M. del Carmen Vargas-Gonzalez
Published
1 January 2008
Publication details
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (5163 LNCS), PART 1, pp. 155-164
Links
DOI PDF

 

Abstract

A method to estimate the probability density function of multivariate distributions is presented. The classical Parzen window approach builds a spherical Gaussian density around every input sample. This choice of the kernel density yields poor robustness for real input datasets. We use multivariate Student-t distributions in order to improve the adaptation capability of the model. Our method has a first stage where hard neighbourhoods are determined for every sample. Then soft clusters are considered to merge the information coming from several hard neighbourhoods. Hence, a specific mixture component is learned for each soft cluster. This leads to outperform other proposals where the local kernel is not as robust and/or there are no smoothing strategies, like the manifold Parzen windows. © Springer-Verlag Berlin Heidelberg 2008.

Citation

How to cite

E. López-Rubio, J. Ortiz-De-Lazcano-Lobato, D. López-Rodríguez, et al. “Robust nonparametric probability density estimation by soft clustering”. In: Artificial Neural Networks - ICANN 2008 , 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part I. Ed. by V. K. á, R. Neruda and J. Koutník. Vol. 5163 LNCS. Lecture Notes in Computer Science PART 1. cited By 0; Conference of 18th International Conference on Artificial Neural Networks, ICANN 2008 ; Conference Date: 3 September 2008 Through 6 September 2008; Conference Code:73798. Prague: Springer, 2008, pp. 155-164. DOI: 10.1007/978-3-540-87536-9_17. URL: https://doi.org/10.1007/978-3-540-87536-9_17.

BibTeX 
<pre><code>
@InProceedings{LopezRubio2008, author = {E. López-Rubio and J.M. Ortiz-De-Lazcano-Lobato and D. López-Rodríguez and M. {Del Carmen Vargas-Gonzalez}}, booktitle = {Artificial Neural Networks - {ICANN} 2008 , 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part {I}}, title = {Robust nonparametric probability density estimation by soft clustering}, year = {2008}, address = {Prague}, editor = {Vera Kurko á and Roman Neruda and Jan Koutník}, note = {cited By 0; Conference of 18th International Conference on Artificial Neural Networks, ICANN 2008 ; Conference Date: 3 September 2008 Through 6 September 2008; Conference Code:73798}, number = {PART 1}, pages = {155-164}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = {5163 LNCS}, abstract = {A method to estimate the probability density function of multivariate distributions is presented. The classical Parzen window approach builds a spherical Gaussian density around every input sample. This choice of the kernel density yields poor robustness for real input datasets. We use multivariate Student-t distributions in order to improve the adaptation capability of the model. Our method has a first stage where hard neighbourhoods are determined for every sample. Then soft clusters are considered to merge the information coming from several hard neighbourhoods. Hence, a specific mixture component is learned for each soft cluster. This leads to outperform other proposals where the local kernel is not as robust and/or there are no smoothing strategies, like the manifold Parzen windows. © Springer-Verlag Berlin Heidelberg 2008.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icann/Lopez-RubioOLV08.bib}, document_type = {Conference Paper}, doi = {10.1007/978-3-540-87536-9_17}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, keywords = {Data sets; Gaussian density; Input sample; Kernel density; Local kernel; Mixture components; Multivariate distributions; Multivariate Student; Non-parametric; Parzen windows; Probability density estimation; Soft clustering, Backpropagation; Neural networks; Probability distributions; Windows, Probability density function}, source = {Scopus}, url = {https://doi.org/10.1007/978-3-540-87536-9_17}, } 
<button class='copy-bib-btn' id='copy-bib-btn'><i class='bi bi-clipboard'></i> Copy BibTeX</button>

Bibliometric data

The following data has been extracted from resources such as OpenAlex, Dimensions, PlumX or Altmetric.

Robust nonparametric probability density estimation by soft clustering

Cites

The following graph plots the number of cites received by this work from its publication, on a yearly basis.

Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

  1. J. M. Pérez (2009). SINAI : Self-Organizing Neural Systems for Internet TIN 2006-07362.
  2. Unknown Author (n.d.). NA.
  3. Unknown Author (n.d.). NA.
  4. Unknown Author (n.d.). NA.
  5. Unknown Author (n.d.). NA.
  6. Unknown Author (n.d.). NA.