Expectation-Maximization for Sparse and Non-Negative PCA
published: Aug. 7, 2008, recorded: July 2008, views: 939
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on its elements: a cardinality constraint limits the number of non-zero elements, and non-negativity forces the elements to have equal sign. This problem is known as sparse and non-negative principal component analysis (PCA), and has many applications including dimensionality reduction and feature selection. Based on expectation-maximization for probabilistic PCA, we present an algorithm for any combination of these constraints. Its complexity is at most quadratic in the number of dimensions of the data. We demonstrate significant improvements in performance and computational efficiency compared to the state-of-the-art, using large data sets from biology and computer vision.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !