Rethinking PCA for Modern Data Sets: Theory, Algorithms, and Applications [Scanning the Issue]

Namrata Vaswani, Yuejie Chi, Thierry Bouwmans
2018 Proceedings of the IEEE  
In today's big and messy data age, there is a lot of data generated everywhere around us. Examples include texts, tweets, network traffic, changing Facebook connections, or video surveillance feeds coming in from one or multiple cameras. Dimension reduction and noise/outlier removal are usually important preprocessing steps before any high-dimensional (big) data set can be used for inference. A common way to do this is via solving the principal component analysis (PCA) problem or its robust
more » ... nsions. The basic PCA problem has been studied for over a century since the early work by Pearson in 1901 and Hotelling in 1933. The aim of PCA is to reduce the dimensionality of multivariate data while preserving as much of the relevant information as possible. It is often the first step in various types of exploratory data analysis, predictive modeling, and classification and clustering tasks, and finds applications in biomedical imaging, computer vision, process fault detection, recommendation systems' design, and many more domains.
doi:10.1109/jproc.2018.2853498 fatcat:6d52ecsbgfcnxchfeiugxzoerm