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3. Dimensionality reduction
The diversity of algorithms used in dimensionality reduction is due first and foremost to the fact that the objectives of dimensionality reduction vary widely.
If we use this technique simply for visualization purposes, to understand the distribution of a data set, we'll naturally look for an algorithm that best preserves the local or global structure of the data, taking as a reference, for example, the respective distances between each example. If, on the other hand, such a dimensionality reduction is used to feed a classification algorithm, the algorithm will be asked to retain the most discriminating variables for the classification that follows.
3.1 Feature selection
Feature selection is the most direct method of reducing the dimensionality...
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Dimensionality reduction
Density estimation
Bibliography
Software tools
For computations that do not involve deep learning and that deal with data volumes that do not require the use of distributed computing, the two reference software tools are scikit-learn and R
The Spark Mlib library adapts the main machine learning algorithms (excluding deep learning) to a distributed environment, enabling the processing of very large volumes of data.
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