105 citations · 172 across the 26 of their papers we have counts for
5 papers · 1 filter
Nystrom Method for Approximating the GMM Kernel
Ping Li
The GMM (generalized min-max) kernel was recently proposed (Li, 2016) as a measure of data similarity and was demonstrated effective in machine learning tasks. In order to use the…
Linear signal recovery from -bit-quantized linear measurements: precise analysis of the trade-off between bit depth and number of measurements
Martin Slawski, Ping Li
We consider the problem of recovering a high-dimensional structured signal from independent Gaussian linear measurements each of which is quantized to bits. Our interest is in…
Methods for Sparse and Low-Rank Recovery under Simplex Constraints
Ping Li, Syama Sundar Rangapuram, Martin Slawski
The de-facto standard approach of promoting sparsity by means of -regularization becomes ineffective in the presence of simplex constraints, i.e.,~the target is known to ha…
A Comparison Study of Nonlinear Kernels
Ping Li
In this paper, we compare 5 different nonlinear kernels: min-max, RBF, fRBF (folded RBF), acos, and acos-, on a wide range of publicly available datasets. The proposed fRBF ke…
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search
Ping Li, Michael Mitzenmacher, Anshumali Shrivastava
The method of random projections has become a standard tool for machine learning, data mining, and search with massive data at Web scale. The effective use of random projections re…