UC Riverside

Feature selection is a crucial topic in high-dimensional statistics, particularly in various applications ranging from physics, economics, health, to many others. A critical objective is properly controlling the false discovery rate (FDR) to limit the selection of irrelevant features. For pixelated image data, traditional feature selection techniques can be used but they often struggle with high dimensionality, computational inefficiency, and procedural rigidity. This work leverages the use of latent representation learning and the knockoffs filter to succinctly identify key features while controlling for the FDR. We propose ImgKnock, a novel pipeline that integrates deep latent representation learning with the model-X knockoff framework. The pipeline employs four steps: i) generates a reduced set of latent representative features of the image data using self-supervised learning; ii) generates valid knockoffs of the latent features; iii) runs knockoff selection with FDR control; iv) interprets the selected latent features. Using the handwritten MNIST digits and CIFAR-10 image datasets, we are able to control the FDR and get AUC metrics of 0.911 and 0.779 respectively using deep knockoffs. We apply the novel procedure to fundus images to better understand the important features that are associated with glaucoma, a persistent eye disease. Modifying our method to use second-order representative group knockoffs, we are able to control the FDR and return AUC metrics of 0.7478 and 0.7356 in simulated runs with the latent representations of the fundus images. We also develop several tools that monitor the adherence of knockoff properties and analyze the latent representations. Future research should focus on extending interpretability and enhancing feature selection for multi-label classification tasks. This dissertation is organized as follows. Chapter 1 introduces the knockoffs framework and its application in feature selection, particularly for image data. Chapter 2 delves into the use of knockoffs for multinomial and image data, highlighting both successes and limitations that motivate ImgKnock using latent features. Chapter 3 details the methodology and rationale behind ImgKnock, supported by simulated experiments demonstrating FDR control. Chapter 4 showcases the use of ImgKnock on glaucoma detection, with several modifications. Finally, Chapter 5 summarizes the work and discusses future research directions.