What is PCA, KPCA and ICA used for?

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
• PCA can be used for projecting and visualizing data in lower dimensions.
• 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
• In PCA the number of input dimensiona are equal to principal components
• PCA is
•               performs a PCA with non-linearly separable data sets.
• What would you do in PCA to get the same projection as SVD?
• ________performs a PCA with non-linearly separable data sets.
• Which of the following is / are true about weak learners used in ensemble model? 1. They have low variance and they don’t usually overfit 2. They have high bias, so they can not solve hard learning problems 3. They have high variance and they don’t usually overfit
• Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly separable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?