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Q. |
## In PCA the number of input dimensiona are equal to principal components |

A. | true |

B. | false |

Answer» A. true |

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- 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.