Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. In particular, r...
High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensiona...
James E. Fowler, Qian Du, Wei Zhu, Nicolas H. Youn...
Random projection has been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. It represents a computati...
Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, Yas...
We consider the problem of classification in nonadaptive dimensionality reduction. Specifically, we bound the increase in classification error of Fisher’s Linear Discriminant...
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...