An iterative method for classification of binary data
This work addresses the challenge of efficient inference on compressed binary data, but it is incremental as it builds on an existing framework.
The paper tackles the problem of improving classification accuracy for binary data by proposing an iterative application of a simple classification framework, which also serves as a preprocessing step to enhance other methods like support vector machines, with theoretical guarantees demonstrated in simple settings.
In today's data driven world, storing, processing, and gleaning insights from large-scale data are major challenges. Data compression is often required in order to store large amounts of high-dimensional data, and thus, efficient inference methods for analyzing compressed data are necessary. Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application. As a side consequence, we show that the original framework can be used as a data preprocessing step to improve the performance of other methods, such as support vector machines. For several simple settings, we showcase the ability to obtain theoretical guarantees for the accuracy of the iterative classification method. The simplicity of the underlying classification framework makes it amenable to theoretical analysis and studying this approach will hopefully serve as a step toward developing theory for more sophisticated deep learning technologies.