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What does dimensionality reduction mean exactly?

I searched for its meaning, I just found that it means the transformation of raw data into a more useful form. So what is the benefit of having data in a useful form, I mean how can I use it in practical life (application)?

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Dimensionality reduction is the process of reducing the number of random variables of the program under consideration, by obtaining a set of principal variables. For example,  a simple email classification problem, where we need to classify whether the email is spam or not. This can involve a large number of features, such as whether or not the e-mail has a generic title, the content of the e-mail, whether the email uses a template, etc. Here is the diagrammatic representation of dimensionality reduction:

                                    image

 It can be divided into:

  • Feature selection:  We find a subset of the original set of variables, or features, to get a smaller subset that can be used to model the problem. It usually involves three ways:

  1. Filter

  2. Wrapper

  3. Embedded

  • Feature extraction: This reduces the data in a high dimensional space to a lower dimension space, i.e. a space with lesser no. of dimensions.

You can refer the following link for more information: https://en.wikipedia.org/wiki/Dimensionality_reduction

Dimensionality reduction is a Machine Learning Technique of diminishing the measure of Random Variables in a Problem by acquiring a lot of head Variables. So for more insights about Dimensionality reduction visit this Machine Learning Course.

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