Understanding Independent Component Analysis (ICA) in Machine Learning
Independent Component Analysis (ICA) is a powerful technique used in machine learning to separate mixed signals into independent sources. It is similar to being at a party with multiple conversations happening simultaneously and being able to focus on one conversation. This blog post will provide an overview of ICA, its computational method, and its importance in various applications.
Introduction
Imagine you are a detective trying to solve a complex crime. Your job is to gather as much evidence as possible and separate the crucial information from the noise. But what if the pieces of evidence are all mixed up and intertwined, making it difficult to identify the important clues?
This is analogous to the challenge faced in the field of signal processing when dealing with multivariate signals. Multivariate signals involve multiple statistical outcomes at a time, creating a complex web of information. Separating and understanding each individual component of these signals is crucial in various applications…
