Deep learningConvolutional neural networkRegularization methods are often employed to reduce overfitting of machine learning models. Nonconvex penalty functions are often considered for regularization because of
Operator Theory for Analysis of Convex Optimization Methods in Machine Learning(机器学习凸优化方法分析的算子理论) 热度: Non-convexOptimizationforMachine Learning:Design,Analysis,and Understanding TengyuMa ADissertation PresentedtotheFaculty ofPrincetonUniversity ...
丛书:Foundations and Trends® in Machine Learning ISBN:9781680833690 豆瓣评分 目前无人评价 内容简介· ··· Prateek Jain and Purushottam Kar (2017), "Non-convex Optimization for Machine Learning", Foundations and Trends® in Machine Learning: Vol. 10: No. 3-4, pp 142-363. http://dx.d...
接下来,我分两个情况来讨论收敛性:1.Convex。2. Strongly convex。 1.1.convex case 定理1.1(Nonsmooth + convex)如果函数 f 是凸的且是Lipschitzness的。对于迭代方法(1.1),步长选择策略为: \alpha_k =\frac{f(x^k) - f^*}{\|g^k\|^2} 如果g^k \neq 0 ,否则 \alpha_k = 1 。那么我们有:...
In many statistical learning problems, the target functions to be optimized are highly non-convex in various model spaces and thus are difficult to analyze. In this paper, we compute \\emph{Energy Landscape Maps} (ELMs) which characterize and visualize an energy function with a tree structure,...
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation (2018) Non-convex Optimization for Machine Learning (2017) 具有隐凸性或解析解的问题 These slides summarize lots of them. Blind Deconvolution using Convex Programming (2012) Intersecting Faces: Non-negative Matrix Factorization ...
is a special kind of nonconvex function and the non-convexity only comes from the factorization of \(\mathbf {u}\mathbf {u}^t\) . based on this observation, we exploit the special curvature of \(g(\mathbf {u})\) in this section. the existing works proved the local linear ...
Finally, we investigate the proposed algorithms for an important problem in machine learning: the t-distributed stochastic neighbor embedding. Abstract We address the problem of minimizing the sum of a nonconvex, differentiable function and composite functions by DC (Difference of Convex functions) prog...
We assume that the latter functionis a composition of a proper closed function $P$ and a surjective linear map$\\cal M$, with the proximal mappings of $au P$, $au > 0$, simple tocompute. This problem is nonconvex in general and encompasses many importantapplications in engineering and ...
On the behaviour of the Douglas-Rachford algorithm for minimizing a convex function subject to a linear constraint (2019) On The Geometric Analysis of A Quartic-quadratic Optimization Problem under A Spherical Constraint (2019) Gradient Flows and Accelerated Proximal Splitting Methods (2019) Path Lengt...