CS 451 Quiz 21
PCA, Gaussian distributions, and density estimation
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Which 3 of the following 6 quantities are identical? *
How do we measure the total variation in the data? *
How can we choose a good value for k? *
How can we efficiently compute the ratio of the average squared reprojection error and the total variation in the data, for different values of k? *
If we want to use PCA for speeding up supervised learning, of which dataset should we compute the principal components? *
Why is PCA not a good way to address overfitting? *
Anomaly detection and Gaussian distributions
In anomaly detection – like in classification – we try to learn a decision boundary, but we are only given one type of training examples (e.g., only negative examples) *
The maximum value of a Gaussian distribution (for any mu and any sigma) is always 1 *
Given a dataset {x1, x2, ..., xn}, where each xi is a real number, what does (Gaussian) density estimation do? *
For which problems would anomaly detection be a suitable algorithm? *
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