Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

Neil D. Lawrence; Bernhard Schölkopf

edit

Back to publications

Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

Neil D. Lawrence, Bernhard Schölkopf

Proceedings of the International Conference in Machine Learning, Morgan Kauffman 18, 2001.

Abstract

Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for constructing a kernel Fisher discriminant (KFD) from training examples with noisy labels. The approach allows to associate with each example a probability of the label being flipped. We utilise an expectation maximization (EM) algorithm for updating the probabilities. The E-step uses class conditional probabilities estimated as a by-product of the KFD algorithm. The M-step updates the flip probabilities and determines the parameters of the discriminant. We have applied the approach to two real-world data-sets. The results show the feasibility of the approach.

Links

Cite this Paper

BibTeX


@InProceedings{Lawrence:noisy01,
  title = 	 {Estimating a Kernel {F}isher Discriminant in the Presence of Label Noise},
  author = 	 {Lawrence, Neil D. and Schölkopf, Bernhard},
  booktitle = 	 {Proceedings of the International Conference in Machine Learning},
  year = 	 {2001},
  editor = 	 {Brodley, Carla and Danyluk, Andrea P.},
  volume = 	 {18},
  address = 	 {San Francisco, CA},
  publisher =    {Morgan Kauffman},
  pdf = 	 {https://inverseprobability.com/publications/files/noisyfisher.pdf},
  url = 	 {/publications/lawrence-noisy01.html},
  abstract = 	 {Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for constructing a kernel Fisher discriminant (KFD) from training examples with *noisy labels*. The approach allows to associate with each example a probability of the label being flipped. We utilise an expectation maximization (EM) algorithm for updating the probabilities. The E-step uses class conditional probabilities estimated as a by-product of the KFD algorithm. The M-step updates the flip probabilities and determines the parameters of the discriminant. We have applied the approach to two real-world data-sets. The results show the feasibility of the approach.}
}

Endnote

%0 Conference Paper
%T Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
%A Neil D. Lawrence
%A Bernhard Schölkopf
%B Proceedings of the International Conference in Machine Learning
%D 2001
%E Carla Brodley
%E Andrea P. Danyluk	
%F Lawrence:noisy01
%I Morgan Kauffman
%U /publications/lawrence-noisy01.html
%V 18
%X Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for constructing a kernel Fisher discriminant (KFD) from training examples with *noisy labels*. The approach allows to associate with each example a probability of the label being flipped. We utilise an expectation maximization (EM) algorithm for updating the probabilities. The E-step uses class conditional probabilities estimated as a by-product of the KFD algorithm. The M-step updates the flip probabilities and determines the parameters of the discriminant. We have applied the approach to two real-world data-sets. The results show the feasibility of the approach.

RIS


TY  - CPAPER
TI  - Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
AU  - Neil D. Lawrence
AU  - Bernhard Schölkopf
BT  - Proceedings of the International Conference in Machine Learning
DA  - 2001/01/01
ED  - Carla Brodley
ED  - Andrea P. Danyluk	
ID  - Lawrence:noisy01
PB  - Morgan Kauffman
VL  - 18
L1  - https://inverseprobability.com/publications/files/noisyfisher.pdf
UR  - /publications/lawrence-noisy01.html
AB  - Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for constructing a kernel Fisher discriminant (KFD) from training examples with *noisy labels*. The approach allows to associate with each example a probability of the label being flipped. We utilise an expectation maximization (EM) algorithm for updating the probabilities. The E-step uses class conditional probabilities estimated as a by-product of the KFD algorithm. The M-step updates the flip probabilities and determines the parameters of the discriminant. We have applied the approach to two real-world data-sets. The results show the feasibility of the approach.
ER  -

APA


Lawrence, N.D. & Schölkopf, B.. (2001). Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. Proceedings of the International Conference in Machine Learning 18 Available from /publications/lawrence-noisy01.html.