Large Dimensional Analysis of LS-SVM Transfer Learning: Application to Polsar Classification

Cyprien Doz; Chengfang Ren; Jean Philippe Ovarlez; Romain Couillet

ICASSP 2023accepted0 citations

Large Dimensional Analysis of LS-SVM Transfer Learning: Application to Polsar Classification

Cyprien Doz, Chengfang Ren, Jean Philippe Ovarlez, Romain Couillet

Abstract

This article analyzes a kernel-based transfer learning method, under a k-class Gaussian mixture model for the input data. Following recent advances in random matrix theory, we propose new insights in transfer learning schemes for challenging cases, when the first-order statistics of all data classes coincide. The article proves the asymptotic normality of the LS-SVM decision function for any smooth kernel function. As a result, an optimization scheme is proposed to minimize the classification error rate. Our theoretical results are corroborated through simulations and then successfully applied to the context of transfer learning for PolSAR image classification.

BibTeX

@inproceedings{icassp2023_largedimensional,
  title = {Large Dimensional Analysis of LS-SVM Transfer Learning: Application to Polsar Classification},
  author = {Cyprien Doz and Chengfang Ren and Jean Philippe Ovarlez and Romain Couillet},
  booktitle = {ICASSP 2023},
  year = {2023}
}