@inproceedings{df72b3e3432f489287b3153031cd6109,
title = "Speed up kernel projection vector machine using kronecker decomposition",
abstract = "We present a speedup algorithm for kernel projection vector machine (KPVM) based on kronecker product decomposition. The large scale kernel matrix K with size of n × n is factorized into two small matrices K1 and K2 with size n1 × n1 and n2 × n2 respectively where n1 × n2 = n. The time-consuming SVD operation on K in KPVM is calculated through K 1 and K2. The computation complexity is reduced to O(n2\} from O(n3) originally while generalization ability is undiminished or even better than KPVM.",
keywords = "Kronecker product, Neural network, Projection vector machine",
author = "Deng, \{Wan Yu\} and Kai Zhang and Qinghua Zheng",
year = "2012",
language = "英语",
isbn = "9788994364193",
series = "Proceedings - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012",
pages = "722--725",
booktitle = "Proceedings - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012",
note = "2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012 ; Conference date: 23-10-2012 Through 25-10-2012",
}