Dimensionality reduction techniques help in simplifying complex data from "summary" of Machine Learning by Stephen Marsland
Dimensionality reduction techniques are essential tools in the field of machine learning as they help in simplifying complex data. These techniques work by reducing the number of random variables under consideration, which in turn reduces the computational complexity of the problem. In many real-world applications, data is often high-dimensional, making it challenging to analyze and interpret. By reducing the dimensionality of the data, we can remove noise, redundant information, and irrelevant features, leading to a more concise and meaningful representation of the data.
One common approach to dimensionality reduction is principal component analysis (PCA), which aims to find the orthogonal directions in which the data varies the most. By projecting the data onto th...
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