Kernel Machines are Not Black Boxes - On the Interpretability of Kernel-based Nonparametric Models
Year: 2014
Type: dissertation
Abstract: Kernel-based nonparametric models are often perceived as uninterpretable black boxes. This notion is however questionable; permutation-based variable importance, for example, is one approach for interpreting various nonparametric models. Unfortunately, besides being computationally intensive, permutation-based variable importance is merely a predictor importance measure for supervised regression analysis, and is inapplicable for unsupervised models. This thesis overcomes these issues by using the gradient of kernel-based nonparametric functions to interpret kernel-based models. In particular, the gradient of a kernel regression function can produce variable importance scores with significant computational advantage over permutation-based variable importance. Gradient information can further reveal predictor-response association structure through dimension reduction and graphical analysis; a property not enjoyed by variable importance scores. Most importantly, kernel methods embody a family of supervised and unsupervised learning techniques, and gradient information can be used to interpret not only supervised kernel models, but also unsupervised ones. This fact is demonstrated by using gradient information to interpret the co-dependence structures, as discovered by kernel canonical correlation analysis, between two variable sets.
Source: TSpace (University of Toronto)
Author: Billy Chang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2588180856