Dimensionality reduction is the process of reducing the number of input features or variables in a dataset. This is important for several reasons: it can help to remove noise and redundancy in the data, make the data easier to visualize and understand, and reduce the computational cost of training machine learning models.
There are several dimensionality reduction algorithms, but they can be broadly classified into two categories: feature selection and feature extraction.
- Feature selection: In feature selection, we select a subset of the original features that are most relevant to the problem at hand. There are several techniques for feature selection, including correlation-based feature selection, mutual information-based feature selection, and wrapper-based feature selection.
- Feature extraction: In feature extraction, we create a new set of features that are a linear combination of the original features. The new features are chosen to maximize the variance of the data, while minimizing the correlation between them. The most common technique for feature extraction is principal component analysis (PCA).
Other dimensionality reduction techniques include linear discriminant analysis (LDA), t-distributed stochastic neighbor embedding (t-SNE), and auto encoders.
Dimensionality reduction algorithms are widely used in machine learning applications, particularly in areas such as computer vision, natural language processing, and bioinformatics. They are particularly useful in situations where the data is high-dimensional and complex, and where the number of features is larger than the number of observations.