A generalization of principal component analysis to the exponential family Export

@misc{citeulike:2891300, abstract = {Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA implicitly minimizes a squared loss function, which may be inappropriate for data that is not real-valued, such as binary-valued data. This paper draws on ideas from the Exponential family, Generalized linear models, and Bregman distances, to give a generalization of PCA to loss functions that we argue are better suited to other data types. We describe algorithms for minimizing the loss...}, author = {Collins, M. and Dasgupta, S. and Schapire, R.}, citeulike-article-id = {2891300}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9665}, keywords = {bregman\_divergence, dimensional\_reduction, exponential\_family, pca}, posted-at = {2008-06-17 13:57:03}, priority = {2}, title = {A generalization of principal component analysis to the exponential family}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9665}, year = {2001} }

TY - GEN ID - citeulike:2891300 L3 - citeulike-article-id:2891300 N2 - Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA implicitly minimizes a squared loss function, which may be inappropriate for data that is not real-valued, such as binary-valued data. This paper draws on ideas from the Exponential family, Generalized linear models, and Bregman distances, to give a generalization of PCA to loss functions that we argue are better suited to other data types. We describe algorithms for minimizing the loss... TI - A generalization of principal component analysis to the exponential family KW - bregman_divergence KW - dimensional_reduction KW - exponential_family KW - pca AU - Collins, M AU - Dasgupta, S AU - Schapire, R PY - 2001/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9665 ER -