Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors
Abstract
Recently, covariance descriptors have received much attention as powerful representations of set of points. In this research, we present a new metric learning algorithm for covariance descriptors based on the Dykstra algorithm, in which the current solution is projected onto a half-space at each iteration, and runs at $O(n^3)$ time. We empirically demonstrate that randomizing the order of half-spaces in our Dykstra-based algorithm significantly accelerates the convergence to the optimal solution. Furthermore, we show that our approach yields promising experimental results on pattern recognition tasks.
Cite
Text
Matsuzawa et al. "Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46466-4_47Markdown
[Matsuzawa et al. "Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/matsuzawa2016eccv-stochastic/) doi:10.1007/978-3-319-46466-4_47BibTeX
@inproceedings{matsuzawa2016eccv-stochastic,
title = {{Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors}},
author = {Matsuzawa, Tomoki and Relator, Raissa and Sese, Jun and Kato, Tsuyoshi},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {786-799},
doi = {10.1007/978-3-319-46466-4_47},
url = {https://mlanthology.org/eccv/2016/matsuzawa2016eccv-stochastic/}
}