1. A Generalization of PCA to the Exponential Family Collins, Dasgupta and Schapire Presented by Guy Lebanon

2. Two Viewpoints of PCA Algebraic Given data find a linear transformation such that the sum of squared distances is minimized (over all linear transformation ) Statistical Given data assume that each point is a random variable. Find the maximum likelihood estimator under the constraint that are in a K dimensional subspace and are linearly related to the data.

3. The Gaussian assumption may be inappropriate – especially if the data is binary valued or non-negative for example. Suggestion: replace the Gaussian distribution by any exponential distribution. Given data such that each point comes from an exponential family distribution, find the MLE for under the assumption that it lies in a low dimensional subspace.

4. The new algorithm finds a linear transformation in the parameter space but a nonlinear subspace in the original coordinates. The loss functions may be cast in terms of Bregman distances. The loss function is not convex in the general case. The authors use the alternating minimization algorithm (Csiszar and Tsunadi) to compute the transformation.