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L1-regularization path algorithm for generalized linear models
Generalized linear model Lasso Path algorithm Predictor–corrector method Regularization Variable selection
2015/8/21
We introduce a path following algorithm for L1-regularized generalized linear models. The L 1-regularization procedure is useful especially because it, in effect, selects variables according to the am...
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections
dimension reduction l1 norm Johnson-Lindenstrauss (JL) lemma Cauchy random projections
2015/8/21
For1 dimension reduction in the l1 norm, the method of Cauchy random projections multiplies the original data matrix A ∈ Rn×D with a random matrix R ∈ RD×k (k D) whose entries are i.i.d. samples of ...
The l1 norm of a discrete-time system determines its peak gain, whereas its H_infinity norm determines its RMS gain. In many cases it may be desirable to make a certain map small in the peak gain sens...
Robust solutions to l1, l2, and l_infinity uncertain linear approximation problems using convex optimization
Linear random and uncertain data convex optimization
2015/8/11
We present minimax and stochastic formulations of some linear approximation problems with uncertain data in R^n equipped with the Euclidean (l2), Absolute-sum (l1) or Chebyshev (l-infinity) norms. We ...
Enhancing sparsity by reweighted l1 minimization
1-Minimization ·Iterative reweighting Underdetermined systems of linear equations·Compressive sensing Dantzig selector· Sparsity FOCUSS
2015/8/10
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
A differential equations approach to l1-minimization with applications to array imaging
differential equations approach l1-minimization array imaging
2015/7/14
We present an ordinary differential equations approach to the analysis of algorithms for constructing l1 minimizing solutions to underdeter mined linear systems of full rank. It involves a relaxed min...
Face Recognition from Incomplete Measurements via l1-Optimization
Sparse Representation l1Minimization Face Recognition Sparse Recovery Interior Point Methods Sparse Regularization
2013/1/30
In this work, we consider a homotopic principle for solving large-scale and dense l1underdetermined problems and its applications in image processing and classification. We solve the face recognition ...
Finding approximately rank-one submatrices with the nuclear norm and l1 norm
rank-one submatrices the nuclear norm and l1 norm
2010/11/15
We propose a convex optimization formulation with the nuclear norm and $\ell_1$-norm to find a large approximately rank-one submatrix of a given nonnegative matrix. We develop optimality conditions f...
Renormalized and Entropy Solutions for Nonlinear Parabolic Equations with Variable Exponents and L1 Data
Variable exponents Renormalized solutions Entropy solutions Exist- ence Uniqueness
2015/5/4
Abstract. In this paper we prove the existence and uniqueness of both renormalized solutions and entropy solutions for nonlinear parabolic equations with variable exponents and L 1 data. And moreover,...
线性规划原仿射尺度算法及其在l1优化理论中的应用
2007/7/28
期刊信息
篇名
线性规划原仿射尺度算法及其在l1优化理论中的应用
语种
中文
撰写或编译
撰写
作者
陈渊,吴俊,褚健
第一作者单位
浙江大学
刊物名称
上海交通大学学报
页面
1996,30(suppl.1),185-187
出版日期
1996年
月
日
文章标识(ISSN)
相关项目
H2/l1混合优化控制的设计理论及应用研究
Basic Properties and Multipliers Space on L1(G)\cap L(p,q)(G) Spaces
Basic Properties Multipliers Space L1(G)\cap L(p,q)(G) Spaces
2010/2/25
Let G be locally compact Abelian group with Haar measure. First is discussed some properties of L1(G) \cap L(p,q)(G) spaces. Then is mentioned the multipliers space on L1(G) \cap L(p,q) (G) spaces.
Proximinality in L1(I,X)
Proximinal Banach spaces
2010/2/25
Let X be a Banach space and let (I,W ,m) be a measure space. For 1 \leq p < \infty, let Lp(I,X) denote the space of Bochner p-integrable functions defined on I with values in X. The object of this pa...
Maximum Entropy-Based Fuzzy Clustering by Using L1-norm Space
Fuzzy c-means Maximum Entropy Structure Strength Number of Clusters L1-norm Space
2010/2/26
One of the most important methods in analysis of large data sets is clustering. These methods are not only major tools to uncover the underlying structures of a given data set, but also promising tool...
WEAK\star- INVARIANTLY COMPLEMENTED SUBSPACES OF L\infty(1/w) AND IDEALS OF L1(w) WITH A BOUNDED APPROXIMATE IDENTITY
INVARIANTLY COMPLEMENTED SUBSPACES IDEALS OF L1(w) L\infty(1/w)
2010/3/17
For a locally compact group G let L1(w) be the weighted group algebra and let X be a weak \star-closed translation invariant subspace of L\infty (1/w). In this paper for a certain class of functions w...