Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs
This work addresses a specific bottleneck in supervised regression and classification tasks using GSFA, offering incremental improvements over existing graph methods.
The authors tackled the problem of improving estimation accuracy in graph-based slow feature analysis (GSFA) by proposing the exact label learning (ELL) method, which creates training graphs that explicitly code label values, resulting in higher accuracy for tasks like gender estimation from faces and traffic sign classification.
Slow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a time series. Graph-based SFA (GSFA) is a supervised extension that can solve regression problems if followed by a post-processing regression algorithm. A training graph specifies arbitrary connections between the training samples. The connections in current graphs, however, only depend on the rank of the involved labels. Exploiting the exact label values makes further improvements in estimation accuracy possible. In this article, we propose the exact label learning (ELL) method to create a graph that codes the desired label explicitly, so that GSFA is able to extract a normalized version of it directly. The ELL method is used for three tasks: (1) We estimate gender from artificial images of human faces (regression) and show the advantage of coding additional labels, particularly skin color. (2) We analyze two existing graphs for regression. (3) We extract compact discriminative features to classify traffic sign images. When the number of output features is limited, a higher classification rate is obtained compared to a graph equivalent to nonlinear Fisher discriminant analysis. The method is versatile, directly supports multiple labels, and provides higher accuracy compared to current graphs for the problems considered.