Research Information System
2025 International Conference on Sampling Theory and Applications-SampTA, Vienna, Austria, 28 July - 01 August 2025, (Full Text)
In high-dimensional classification tasks, data from different classes often lie in a union of lower-dimensional subspaces. Identifying the basis vectors for each subspace that effectively differentiates between classes can enhance the explainability and accuracy of classification methods. This study proposes a novel approach that uses singular value decomposition to identify class-specific basis vectors that maximize the separability of classes. Instead of selecting the most significant n number of basis vectors using traditional heuristics for basis selection, the mean average precision for each basis vector is calculated, and the topperforming n basis vectors are selected. Furthermore, this study extends the methodology by integrating feature vector outputs from two different pre-trained deep learning models as input for classification evaluation in two different cases. The proposed methodology is validated through simulations, demonstrating its potential for improving classification in high-dimensional spaces.