Dimensiovähennysmenetelmät

Dimensiovähennysmenetelmät, known in English as dimensionality reduction techniques, are a set of methods used in data analysis and machine learning to reduce the number of random variables under consideration, by obtaining a set of principal variables. These techniques are essential when dealing with high-dimensional data, where the number of features or variables is very large compared to the number of observations. High dimensionality can lead to the "curse of dimensionality," making algorithms slower, less accurate, and harder to interpret.

The primary goals of dimensionality reduction are to simplify data, reduce storage space, speed up computations, and improve the performance of machine learning models by removing noise and redundancy. There are two main categories of dimensionality reduction techniques: feature selection and feature extraction.

Feature selection involves choosing a subset of the original features that are most relevant to the problem at hand, while discarding the rest. This method preserves the original meaning of the features.

Feature extraction, on the other hand, transforms the original features into a new, smaller set of features. These new features are often combinations or projections of the original ones and may not have a direct, easily interpretable meaning. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are popular examples of feature extraction methods. PCA aims to find a set of orthogonal linear transformations that capture the maximum variance in the data, while t-SNE is particularly effective for visualizing high-dimensional data in a low-dimensional space, preserving local structure.