1. Principal Component Analysis Machine Learning

2. Last Time Expectation Maximization in Graphical Models – Baum Welch

3. Now Unsupervised Dimensionality Reduction

4. Curse of Dimensionality In (nearly) all modeling approaches, more features (dimensions) require (a lot) more data – Typically exponential in the number of features This is clearly seen from filling a probability table. Topological arguments are also made. – Compare the volume of an inscribed hypersphere to a hypercube

5. Dimensionality Reduction We’ve already seen some of this. Regularization attempts to reduce the number of effective features used in linear and logistic regression classifiers

6. Linear Models When we regularize, we optimize a function that ignores as many features as possible. The “effective” number of dimensions is much smaller than D

7. Support Vector Machines In exemplar approaches (SVM, k-nn) each data point can be considered to describe a dimension. By selecting only those instances that maximize the margin (setting α to zero), SVMs use only a subset of available dimensions in their decision making.

8. Decision Trees Decision Trees explicitly select split points based on features that improve InformationGain or Accuracy Features that don’t contribute to the classification sufficiently are never used. weight <165 5M height <68 5F 1F / 1M

9. Feature Spaces Even though a data point is described in terms of N features, this may not be the most compact representation of the feature space Even classifiers that try to use a smaller effective feature space can suffer from the curse-of-dimensionality If a feature has some discriminative power, the dimension may remain in the effective set.

10. 1-d data in a 2-d world

11. Dimensions of high variance

12. Identifying dimensions of variance Assumption: directions that show high variance represent the appropriate/useful dimension to represent the feature set.

13. Aside: Normalization Assume 2 features: – Percentile GPA – Height in cm. Which dimension shows greater variability?

14. Aside: Normalization Assume 2 features: – Percentile GPA – Height in cm. Which dimension shows greater variability?

15. Aside: Normalization Assume 2 features: – Percentile GPA – Height in m. Which dimension shows greater variability?

16. Principal Component Analysis Principal Component Analysis (PCA) identifies the dimensions of greatest variance of a set of data.

17. Eigenvectors Eigenvectors are orthogonal vectors that define a space, the eigenspace. Any data point can be described as a linear combination of eigenvectors. Eigenvectors of a square matrix have the following property. The associated lambda is the eigenvalue.

18. PCA Write each data point in this new space To do the dimensionality reduction, keep C < D dimensions. Each data point is now represented as a vector of c’s.

19. Identifying Eigenvectors PCA is easy once we have eigenvectors and the mean. Identifying the mean is easy. Eigenvectors of the covariance matrix, represent a set of direction of variance. Eigenvalues represent the degree of the variance.

20. Eigenvectors of the Covariance Matrix Eigenvectors are orthonormal In the eigenspace, the Gaussian is diagonal – zero covariance. All eigen values are non-negative. Eigenvalues are sorted. Larger eigenvalues, higher variance

21. Dimensionality reduction with PCA To convert from an original data point to PCA To reconstruct a point

22. Eigenfaces Encoded then Decoded. Efficiency can be evaluated with Absolute or Squared error

23. Some other (unsupervised) dimensionality reduction techniques Kernel PCA Distance Preserving Dimension Reduction Maximum Variance Unfolding Multi Dimensional Scaling (MDS) Isomap

24. Next Time – Model Adaptation and Semi-supervised Techniques Work on your projects.