# Speed-up and multi-view extensions to Subclass Discriminant Analysis

## TL;DR

This paper introduces a faster subclass discriminant analysis method and a multi-view extension, leveraging graph embedding and spectral regression to improve efficiency and performance on multiple datasets.

## Contribution

It presents a novel speed-up technique based on graph structures and a new multi-view formulation for subclass discriminant analysis, both offering efficient solutions.

## Key findings

- Achieves competitive or superior accuracy compared to existing methods.
- Significantly reduces training time for subclass discriminant analysis.
- Effective on both single-view and multi-view datasets.

## Abstract

In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based on graph embedding and spectral regression approaches that involve eigendecomposition of the corresponding Laplacian matrix and regression to its eigenvectors. We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process. Furthermore, we formulate a novel criterion for multi-view subclass discriminant analysis and show that an efficient solution for it can be obtained in a similar to the single-view manner. We evaluate the proposed methods on nine single-view and nine multi-view datasets and compare them with related existing approaches. Experimental results show that the proposed solutions achieve competitive performance, often outperforming the existing methods. At the same time, they significantly decrease the training time.