FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis

Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA often thought as dimensionality reduction method, purpose actually two-fold: dimension and uncorrelated feature Furthermore, enormity dimensions sample size modern day datasets have rendered centralized solutions unusable. In that vein, this paper reconsiders problem when samples are distributed across nodes an arbitrarily connected network. few for exist, those either overlook learning aspect PCA, tend to high communication overhead makes them inefficient and/or lack ‘exact’ or ‘global’ convergence guarantees. To overcome these aforementioned issues, proposes algorithm termed FAST-PCA (Fast exAct diSTributed PCA) . The proposed efficient terms proven converge linearly exactly principal components, leading well features. claims further supported by experimental results.