What is dimensionality reduction?
Dimensionality reduction is the process of transforming a high-dimensional data set, into a more compact representation with fewer features.
Dimensionality reduction is the process of transforming a high-dimensional data set, into a more compact representation with fewer features.
Principal Component Analysis (PCA) is a dimension reduction technique.
Kernel PCA extends regular PCA to situations where linear transformations are not satisfactory in capturing the variability within the data.
ICA is a specialized dimensionality reduction technique that is used for finding independent components within a multivariate signal.
Factor Analysis is a dimensionality reduction technique that, like PCA, attempts to explain the variability across a set of features
T-SNE is best suited for visualizing high-dimensional data in only two dimensions.
PCA is a linear dimensionality reduction technique while T-SNE is a non-linear technique
Random Projection is a dimensionality reduction technique that maps observations from higher dimensional space into lower dimensional space
PCA is preferable to Random Projection.
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