dimenziócsökkenésbl

Dimenziócsökkenésbl is a Hungarian term that translates to "dimensionality reduction" in English. It refers to the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. This is often done by transforming a large set of variables into a smaller set of variables while retaining most of the information of the original set.

The goal of dimensionality reduction is to simplify datasets, making them easier to visualize, analyze, and model. High-dimensional data can be computationally expensive to process and can suffer from the "curse of dimensionality," where performance degrades as the number of features increases. Dimensionality reduction techniques aim to mitigate these issues.

Common methods for dimensionality reduction include feature selection and feature extraction. Feature selection involves choosing a subset of the original features that are most relevant to the problem. Feature extraction, on the other hand, involves creating new, lower-dimensional features by combining or transforming the original ones. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are well-known examples of feature extraction techniques.

Dimensionality reduction finds applications in various fields, including machine learning, image processing, and data mining. It can improve the efficiency of algorithms, enhance model performance by removing noise and redundancy, and aid in understanding complex datasets by enabling visualization in lower dimensions.