. . . . . . . . allows exploiting the natural sparsity of data while extracting principal components.

[amp_mcq option1=”SparsePCA” option2=”KernelPCA” option3=”SVD” option4=”init parameter” correct=”option1″]

The correct answer is: A. SparsePCA

SparsePCA is a dimensionality reduction technique that allows exploiting the natural sparsity of data while extracting principal components. It does this by using a sparse coding model to represent the data, which can then be used to reconstruct the data with a lower number of principal components.

SparsePCA has been shown to be effective in a variety of applications, including image denoising, face recognition, and text classification.

Here is a brief explanation of each option:

• SparsePCA is a dimensionality reduction technique that allows exploiting the natural sparsity of data while extracting principal components.
• KernelPCA is a dimensionality reduction technique that uses a kernel function to map the data into a higher-dimensional space, where the principal components can be more easily extracted.
• SVD is a mathematical technique that can be used to decompose a matrix into a set of orthogonal matrices. It is often used in dimensionality reduction, but it does not specifically exploit the natural sparsity of data.
• init parameter is a parameter that is used to initialize the algorithm. It is not specific to SparsePCA, and can be used in a variety of other algorithms.