A P-ADMM for sparse quadratic kernel-free least squares semi-supervised support vector machine
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.03.069
Abstract: Abstract In this paper, we propose a sparse quadratic kernel-free least squares semi-supervised support vector machine model by adding an L1 norm regularization term to the objective function and using the least squares method, which… read more here.
Keywords: quadratic kernel; free least; least squares; sparse quadratic ... See more keywords
Sparse Quadratic Approximation for Graph Learning.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2023.3263969
Abstract: Learning graphs represented by M-matrices via an l1-regularized Gaussian maximum-likelihood method is a popular approach, but also one that poses computational challenges for large scale datasets. Recently proposed methods cast this problem as a constrained… read more here.
Keywords: sparse quadratic; graph; approximation graph; method ... See more keywords