We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
In this paper, we study dimension reduction of the three-dimensional (3D) Gross–Pitaevskii equation (GPE) modeling Bose–Einstein condensation under different limiting interact...
Weizhu Bao, Yunyi Ge, Dieter Jaksch, Peter A. Mark...
This work presents the use of sensor stream reduction algorithms in clustered wireless sensor networks (WSNs), where the cluster head node is responsible to reduce the amount of d...
We give a tutorial overview of several geometric methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the da...
In this paper, we propose a new nonlinear dimensionality reduction algorithm by adopting regularized least-square criterion on local areas of the data distribution. We first propo...