In PCA the number of input dimensiona are equal to principal components

Related MCQs

• The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other
• Which of the following is a reasonable way to select the number of principal components "k"?
• PCA can be used for projecting and visualizing data in lower dimensions.
• PCA is
• PCA works better if there is 1. A linear structure in the data 2. If the data lies on a curved surface and not on a flat surface 3. If variables are scaled in the same unit
•               performs a PCA with non-linearly separable data sets.
• What would you do in PCA to get the same projection as SVD?
• What is PCA, KPCA and ICA used for?
• ________performs a PCA with non-linearly separable data sets.
•            allows exploiting the natural sparsity of data while extracting principal components.