Speed-up and multi-view extensions to Subclass Discriminant Analysis
This work addresses computational bottlenecks in discriminant analysis for researchers and practitioners in machine learning, offering incremental improvements in speed and multi-view capabilities.
The paper tackles the computational inefficiency of subclass discriminant analysis by introducing a speed-up method using graph embedding and spectral regression, and extends it to multi-view data with a novel criterion. Experimental results on 18 datasets show competitive performance and significantly reduced training time compared to existing methods.
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.