How is sparse PCA different from a standard Principal Component Analysis?

• Spare PCA is a specialized variant of Principal Component Analysis (PCA) in machine learning that is used in statistical analysis, especially when analyzing multivariate data.

• Sparse PCA is used to reduce the dimensionality of a dataset by introducing sparsity structures in the input features.

• Using the standard PCA, you can only select the most important midrange features, assuming each instance can be rebuilt using the same components.

• By using a sparse PCA, you can use a limited number of components, but without the limitation given by a dense projection matrix.

• This can be done using a sparse matrix, where the number of non-zero elements is quite low.