What is Dimension Reduction?
Dimension reduction is a technique in machine learning that involves reducing the number of features or variables in a dataset without losing important information. This is particularly useful when dealing with high-dimensional data that can be difficult to work with and analyze. Dimension reduction can improve computational efficiency, reduce storage requirements, and help prevent overfitting.
Why Dimension Reduction?
In the contemporary world, the scale of data produced and dealt with is a major concern. As more and more data is produced, the need for more computational power and other infrastructure requirements increases which leads to higher expenses. Hence we often reduce the size without losing important information.
Ways to reduce the dimensions:
There are two main approaches to dimension reduction: feature selection and feature extraction.
Feature selection involves selecting a subset of the original features in the dataset based on their relevance to the problem at hand. This can be done using various statistical methods such as correlation analysis, mutual information, and hypothesis testing. The selected features are then used for modelling and analysis. However, feature selection can sometimes lead to the loss of important information and may not be suitable for complex datasets with high interdependence between features.
Feature extraction, on the other hand, involves transforming the original features into a new set of features that are linearly uncorrelated and capture the most important information in the original features. This is achieved by projecting the original data onto a lower-dimensional subspace that preserves the most important patterns in the data. This can be done using techniques such as principal component analysis (PCA), independent component analysis (ICA), and linear discriminant analysis (LDA). (I would be covering the concepts of PCA and LDA in a separate article soon.)
In conclusion, dimension reduction is an important technique in machine learning that can help improve computational efficiency, reduce storage requirements, and prevent overfitting. Feature selection and feature extraction are the two main approaches to dimension reduction, and there are several techniques available for each approach. Choosing the appropriate technique depends on the nature of the dataset and the problem at hand.
Thank you for reading! I hope this small article helped you with the concept. stay tuned to learn more about PCA and LDA.