Keywords

#dimensionality_reduction

Dimensionality reduction

Process of reducing the number of random variables under consideration

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable. Dimensionality reduction is common in fields that deal with large numbers of observations and/or large numbers of variables, such as signal processing, speech recognition, neuroinformatics, and bioinformatics.

Tue 10th

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Dimensionality reduction Nonlinear dimensionality reduction Multifactor dimensionality reduction Data reduction Curse of dimensionality Machine learning K-nearest neighbors algorithm Trajectory inference Dimensional reduction Sufficient dimension reduction
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