Notes:

Dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of dimensions, or variables, in a dataset. Dimensionality reduction is often used when working with high-dimensional datasets, which can be difficult to analyze and visualize due to the large number of dimensions.

Dimensionality reduction can be achieved in several ways. One common approach is to use feature selection, which involves selecting a subset of the original dimensions that are most relevant or informative for the analysis or prediction task at hand. This can help to reduce the complexity of the dataset, and to improve the performance of machine learning algorithms by removing irrelevant or redundant dimensions.

Another approach to dimensionality reduction is to use dimensionality reduction algorithms, such as principal component analysis (PCA) or singular value decomposition (SVD). These algorithms transform the original dataset into a new, lower-dimensional space, where the dimensions are orthogonal (uncorrelated) and ranked by their importance or significance. This can help to reveal the underlying structure of the data, and to compress the data into a more compact and manageable form.

Overall, dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of dimensions in a dataset. Dimensionality reduction can be achieved through feature selection or dimensionality reduction algorithms, and can help to improve the performance and interpretability of machine learning algorithms, and to reveal the underlying structure of the data.

Wikipedia:

See also:

Dimensionality Reduction & Dialog Systems 2019

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