Making nonlinear manifold learning models interpretable: The manifold grand tour

Abstract

Dimensionality reduction is required to produce visualizations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity and applying linear projections while tumbling the projection axes in a defined sequence which generates a Grand Tour of the data. We propose using smooth nonlinear topographic maps of the data distribution to guide the Grand Tour, increasing the effectiveness of this approach by prioritising the linear views of the data that are most consistent with global data structure in these maps. A further consequence of this approach is to enable direct visualisation of the topographic map onto projective spaces that discern structure in the data. The experimental results on standard databases reported in this paper, using Self-Organising Maps and Generative Topographic Mapping, illustrate the practical value of the proposed approach. The main novelty of our proposal is the definition of a systematic way to guide the search of data views in the grand tour, selecting and prioritizing some of them, based on nonlinear manifold models.

Explaining Support Vector Machines: A Color Based Nomogram

“…Other work consists in visualizing the discrimination of data cohorts by means of projections guided by paths through the data (tours) [ 14 – 16 ]. Although these methods offer additional insights, they do not quantify the impact of each feature on the prediction, which is the goal of the current work.…”