Optimizing Class Separability via Projection-Based Discriminative Basis Selection

Date

2025-01-01

Author

Özçil, İsmail
Koku, Ahmet Buğra
Sekmen, Ali
Bilgin, Bahadir

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

42
views

0
downloads

Cite This

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.

URI

https://hdl.handle.net/11511/117954

Collections

Department of Mechanical Engineering, Conference / Seminar