Image classification is a key task in computer vision, typically driven by high-performing deep learning methods. However, these methods are criticized for lacking transparency. As an alternative, Green Learning offers more interpretable models, though their performance is not as strong as that of deep learning.
We propose a novel Green Learning framework that enhances performance by integrating label supervision directly into the feature extraction process. A key component of our approach is the LDA filter block, which is a feedforward mechanism that uses Linear Discriminant Analysis (LDA) to create convolution filters without the need for backpropagation. Additionally, we present spectral LNT, a new variant of the least-squares normal transform (LNT) that takes advantage of the spatial-spectral structure of feature maps by applying localized linear combinations of features. Together, our methods create a label-guided, interpretable feature extraction pipeline.
