On Wed, Dec 7, 2011 at 9:43 AM, David Warde-Farley <[email protected]> wrote:
> To be precise, (and I hope I got this right lest I confuse things further), a > sparse coding problem with K different training examples and L different > input features and M sparse components corresponds to K independent lasso > problems with L training examples each and M input features. In the doc, it would probably be easier to understand if we make an analogy with regression. A regression problem consists in learning w given X and y: argmin_w ||y - X^T w||^2 w: [n_features, ] X: [n_samples, n_features] y: [n_samples, ] A sparse coding problem consists in learning alpha given D and x: argmin_alpha ||x - D^T alpha||^2 alpha: [n_components, ] D: [n_features, n_components] x: [n_features, ] Therefore, to encode the entire dataset X, we need to to solve n_samples regression problems each with n_features instances and n_components features. When using the square loss, each problem is independent (not when using the hinge loss). Mathieu ------------------------------------------------------------------------------ Systems Optimization Self Assessment Improve efficiency and utilization of IT resources. Drive out cost and improve service delivery. Take 5 minutes to use this Systems Optimization Self Assessment. http://www.accelacomm.com/jaw/sdnl/114/51450054/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
