Dimensionality Reduction is a technique used in the field of Machine Learning that seeks to reduce the number of features that are used in a given data set. This technique is often used when the data is too complex to make sense of in its original form, or where the number of features is too large for the application being used. Dimensionality Reduction can be used to remove irrelevant or redundant features from a data set, or to reduce the effects of noise (unimportant or random values) within a data set.
The main objective of using Dimensionality Reduction is to minimize the number of decisions that need to be made by a system while still getting as close to the original dataset as possible. This can be a useful technique when working with large datasets which can be computationally expensive to process.
One common alternative to Dimensionality Reduction is Feature Selection, which seeks to identify only the features that are important to the problem at hand. Feature Selection eliminates features which do not appear to be significant in the outcome of the process; this approach can reduce a dataset with many features to a much smaller dataset without diminishing the accuracy or effectiveness of the original dataset.
Dimensionality Reduction is also helpful when data is high-dimensional. High-dimensionality refers to data sets with more than three dimensions. By reducing the dimensions of a dataset, unnecessary data can be eliminated without sacrificing too much accuracy, allowing the dataset and the models built upon it to use less computation time and memory.
Dimensionality Reduction can also be used to identify potentially meaningful relationships between features within a dataset. The technique can work by combining correlated features into one feature, or by recoding features into fewer categories. By using Dimensionality Reduction in this way, models built on the new, smaller dataset can better identify patterns and behaviors that may not have been apparent with more features.
Overall, Dimensionality Reduction is a powerful tool in Machine Learning, helping to improve accuracy, reduce computational time and memory consumption, and identify meaningful relationships within data.
