Matrix Theory and Principal Component Analysis (PCA) Applications in Industry

With the rapid advancement of technology, large volumes of data are generated from mobile apps, social media, and sensors. It is very important to clean the data (remove noise) and capture only the relevant data that will help solve the problem. Principal Component Analysis (PCA) is an effective method for reducing data dimensionality. Practical PCA and matrix theory translate linear algebra into workplace results.
Most books on matrix theory and PCA either stay theoretical or skip the mathematics altogether. This book combines theory with real-world workplace applications across different industries. This book is perfect for undergraduates, graduates, data practitioners, and analytics leaders. You will learn how:

PCA is applied to effectively separate small targets from complex backgrounds in the infrared Patch-Image model and its tensor-based counterpart.

Crime is predicted using the Dynamic Model Decomposition (DMD) and the Convolutional Neural Network Long-Short-Term-Memory (CNN_LSTM) model.

The SPEC module in basic multispectral image processing using cloud computing with Google Earth Engine (GEE) implementation.

Matrix theories connecting continuous & discrete systems, deterministic & probabilistic behaviour, and theory with application.

Matrix methods and PCA are powerful applications in the workplace. With editorial leadership from Professor Carol Anne Hargreaves, an expert in data science, AI, and industry consulting, and Professor Balasubramaniam, an expert in mathematics, AI, Image Processing, and quantum computing, this book unites theoretical depth with practical workplace relevance in modern PCA applications.