Dynamic Adaptive Random Projection with Stacking Pseudoinverse Learning for Image Classification

Random projection is a vital dimensionality-reduction technique for high-dimensional data, whose performance heavily relies on the distribution design of its projection matrix. Conventional approaches generate projection matrices with predefined fixed distributions and thus fail to adapt to the complex distributions inherent in image data. This deficiency frequently causes feature loss or computational redundancy, which degrades overall model performance. To tackle this problem, this paper proposes a dynamic adaptive random projection with stacking pseudoinverse learning (DARPSP) for image classification. The core of the proposed method lies in building a fully dynamic adaptive random-projection mechanism. First, a data-feature perception module dynamically captures the underlying data distribution. Second, a parameterized dynamic distribution-control network predicts distribution parameters for the projection matrix to realize adaptive adjustment. A fusion strategy based on feature-importance evaluation is then adopted to refine the combination of multi-distribution projection outputs. Finally, the above components are integrated into a single-hidden-layer stacking pseudoinverse learning architecture to further boost model performance. The experimental results on the MNIST, COIL-20, and Salinas public datasets show that the proposed method outperforms the comparison methods on accuracy, precision, recall, and F1.