[amp_mcq option1=”SparsePCA” option2=”KernelPCA” option3=”SVD” option4=”None of the Mentioned” correct=”option2″]<\/p>\n
The correct answer is: B. KernelPCA<\/strong><\/p>\n Kernel PCA is a dimensionality reduction technique that can be used to perform PCA on non-linearly separable data sets. It does this by mapping the data into a higher dimensional space where the data points are linearly separable. Once the data has been mapped into this higher dimensional space, PCA can be performed on the data in the new space. This results in a lower dimensional representation of the data that is still able to capture the underlying structure of the data.<\/p>\n Sparse PCA is a dimensionality reduction technique that can be used to find a sparse representation of the data. This means that the data can be represented using a small number of features. Sparse PCA is often used in applications where it is important to reduce the number of features in the data, such as in machine learning applications.<\/p>\n SVD is a mathematical technique that can be used to decompose a matrix into a set of orthogonal matrices. SVD can be used for a variety of purposes, including dimensionality reduction. However, SVD cannot be used to perform PCA on non-linearly separable data sets.<\/p>\n None of the other options are correct.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”SparsePCA” option2=”KernelPCA” option3=”SVD” option4=”None of the Mentioned” correct=”option2″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-58206","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n