In this paper, we study the implicit bias of gradient descent for sparse regression.

The non-stationarity nature and the lack of supervision make the problem challenging to be tackled.

Principal Component Analysis (PCA) is a fundamental statistical tool with multiple applications including dimensionality reduction, data visualization, and noise reduction. Naturally, it is a key part of most standard data analysis and ML pipelines. However, when applied to data collected from numerous individuals, such as the U.S. Census data, outcome of PCA might reveal highly