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Q. |
## PCA is |

A. | forward feature selection |

B. | backword feature selection |

C. | feature extraction |

D. | all of the above |

Answer» C. feature extraction |

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