Dimensiobilisointimenetelmät

Dimensiobilisointimenetelmät, often translated as "dimensionality reduction methods" in English, are a set of techniques used in data analysis and machine learning to reduce the number of features or variables in a dataset while retaining as much of the original information as possible. This process is crucial for several reasons. High-dimensional datasets can be computationally expensive to process, prone to overfitting, and difficult to visualize. By reducing dimensionality, these issues can be mitigated.

There are broadly two categories of dimensionality reduction methods: feature selection and feature extraction. Feature selection methods aim to identify and keep a subset of the original features that are most relevant to the problem at hand, discarding the rest. Feature extraction methods, on the other hand, create new, lower-dimensional features by combining or transforming the original features. These new features are often called latent variables or components.

Common techniques within these categories include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Singular Value Decomposition (SVD). PCA is a popular unsupervised method that finds orthogonal components capturing the maximum variance in the data. LDA is a supervised technique that aims to maximize class separability. t-SNE is particularly useful for visualizing high-dimensional data in a low-dimensional space, often two or three dimensions, by preserving local neighborhood structures. The choice of method depends on the specific dataset characteristics and the goals of the analysis.