Kernel Scaling for Manifold Learning and Classification
This work addresses a specific parameter tuning issue in kernel-based machine learning, offering incremental improvements for tasks like classification and manifold learning.
The paper tackles the problem of setting the kernel scale parameter in kernel methods, proposing tailored approaches for manifold learning and classification tasks, and demonstrates high correlation between optimal classification rates and estimated scales on artificial and real datasets.
Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel's scale parameter, also referred to as the kernel's bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold's intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.