A kernel between sets of vectors Export

@inproceedings{citeulike:2213119, abstract = {In various application domains, including image recognition, it is natural to represent each example as a set of vectors. With a base kernel we can implicitly map these vectors to a Hilbert space and fit a Gaussian distribution to the whole set using Kernel PCA. We define our kernel between examples as Bhattacharyya's measure of affinity between such Gaussians. The resulting kernel is computable in closed form and enjoys many favorable properties, including graceful behavior under...}, author = {Kondor, R. and Jebara, T.}, booktitle = {Proc. of ICML-2003, Washington DC.}, citeulike-article-id = {2213119}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.948}, keywords = {latent-semantic-kernels-etc}, posted-at = {2009-10-27 20:22:33}, priority = {2}, title = {A kernel between sets of vectors}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.948}, year = {2003} }

TY - CONF ID - citeulike:2213119 L3 - citeulike-article-id:2213119 N2 - In various application domains, including image recognition, it is natural to represent each example as a set of vectors. With a base kernel we can implicitly map these vectors to a Hilbert space and fit a Gaussian distribution to the whole set using Kernel PCA. We define our kernel between examples as Bhattacharyya's measure of affinity between such Gaussians. The resulting kernel is computable in closed form and enjoys many favorable properties, including graceful behavior under... TI - A kernel between sets of vectors T2 - Proc. of ICML-2003, Washington DC. KW - latent-semantic-kernels-etc AU - Kondor, R AU - Jebara, T PY - 2003/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.948 ER -