弱(L_1,L_2)-BLD映射的正则性

本文首先将文［１］中的ＢＬＤ映射推广为弱（Ｌ１，Ｌ２）－ＢＬＤ映射，并证明了如下正则性结果：存在两个可积指数 Ｐ１＝Ｐ１（ｎ，Ｌ１，Ｌ２）＜ｎ＜ｑ１＝ｑ１（ｎ，Ｌ１，Ｌ２），使得对任意弱（Ｌ１，Ｌ２）－ＢＬＤ映射ｆ∈（Ω，Ｒｎ），都有ｆ∈（Ω，Ｒｎ），即ｆ为（Ｌ１，Ｌ２）－ＢＬＤ映射．     

弱(L1,L2)-BLD映射的正则性

本文首先将文[1]中的BLD映射推广为弱(L1,L2)-BLD映射,并证明了如下正则性结果存在两个可积指数p1=p1(n,L1,L2)＜n＜q1=q1(n,L1,L2),使得对任意弱(L1,L2)-BLD映射,∈W1(Ω,Rn),都有,∈(Ω,Rn),即,为(L1,L2)-BLD映射.     

分裂可行问题的1-范数正则化方法

主要研究了分裂可行问题的1-范数正则化.首先利用1-范数正则化方法,将分裂可行问题转化为无约束优化问题.其次讨论了1-范数正则化解的若干性质,并给出了求解1-范数正则化解的邻近梯度算法.最后通过数值试验验证了算法的可行性和有效性.     

L1正则化Logistic回归在财务预警中的应用

线性模型和广义线性模型已广泛地用于社会经济、生产实践和科学研究中的数据分析和数据挖掘等领域,如公司财务预警,引入L1范数惩罚技术的模型在估计模型系数的同时能实现变量选择的功能,本文将L1范数正则化Logistic回归模型用于上市公司财务危机预报,结合沪深股市制造业ST公司和正常公司的T-2年财务数据开展实证研究,舛比Logistic回归和L2正则化Logistic回归模型进行对比分析.实验结果表明L1正则化Logistic回归模型的有效性,其在保证模型预测精度的同时提高模型的解释性.     

退化(K_1,K_2)拟正则映射的充分条件

研究了退化弱(k1,k2)拟正则映射的正则性.利用H lder不等式、Sobolev空间的空间分析方法,以及内插定理等工具,给出了退化弱(k1,k2)拟正则映射事实上为退化(k1,k2)拟正则映射的一个充分条件,其结果对非退化情形也成立.     

关于空间(k1,k2)-拟正则映射

本文考虑空间 (k1,k2 ) -拟正则映射的 Lp(p >n)可积性 ,以及当 k1→ 1 ,k2 → 0时 p的渐近行为 .     

求解病态问题的一种改进的Tikhonov正则化--(1)正则化方法的建立

对于带有右端扰动数据的第一类紧算子方程的病态问题 ,本文应用正则化子建立了一类新的正则化求解方法 ,称之为改进的Tikonov正则化 ;通过适当选取正则参数 ,证明了正则解具有最优的渐近收敛阶 .与通常的Tikhonov正则化相比 ,这种改进的正则化可使正则解取到足够高的最优渐近阶     

退化弱(L1,L2)-BLD映射的正则性

本文给出空间退化的弱(L1,L2)-BLD映射的定义.利用Hodge分解,弱逆Holder不等式等工具,证明了其正则性结果:对任意满足Ol,使得对任意退化的弱(L1,L2)-BLD映射f∈Wloc1,q1(Ω,Rn),都有f∈Wloc1,p1(Ω,Rn),即f为通常意义下的退化的(L1,L2)-BLD映射.     

用于神经网络权值稀疏化的L_(1/2)正则化方法

在保证适当学习精度前提下,神经网络的神经元个数应该尽可能少(结构稀疏化),从而降低成本,提高稳健性和推广精度.本文采用正则化方法研究前馈神经网络的结构稀疏化.除了传统的用于稀疏化的L1正则化之外,本文主要采用近几年流行的L1/2正则化.为了解决L1/2正则化算子不光滑、容易导致迭代过程振荡这一问题,本文试图在不光滑点的一个小邻域内采用磨光技巧,构造一种光滑化L1/2正则化算子,希望达到比L1正则化更高的稀疏化效率.本文综述了近年来作者在用于神经网络稀疏化的L1/2正则化的一些工作,涉及的神经网络包括BP前馈神经网络、高阶神经网络、双并行前馈神经网络,以及Takagi-Sugeno模糊模型.     

l~P-系数正则化Shannon采样学习算法收敛速度(英文)

研究l~P-系数正则化意义下Shannon采样学习算法的收敛速度估计问题.借助l~P-空间的凸性不等式给出了样本误差和正则化误差的上界估计,并给出了用K-泛函表示的逼近误差估计.将K-泛函的收敛速度估计转化为平移网络逼近问题,在此基础上给出了用概率表示的学习速度.     

A sharp nonasymptotic bound and phase diagram of L 1/2 regularization

We derive a sharp nonasymptotic bound of parameter estimation of the L1/2 regularization.The bound shows that the solutions of the L1/2 regularization can achieve a loss within logarithmic factor of an ideal mean squared error and therefore underlies the feasibility and effectiveness of the L1/2regularization.Interestingly,when applied to compressive sensing,the L1/2 regularization scheme has exhibited a very promising capability of completed recovery from a much less sampling information.As compared with the Lp(0 p 1) penalty,it is appeared that the L1/2 penalty can always yield the most sparse solution among all the Lp penalty when 1/2 ≤ p 1,and when 0 p 1/2,the Lp penalty exhibits the similar properties as the L1/2 penalty.This suggests that the L1/2 regularization scheme can be accepted as the best and therefore the representative of all the Lp(0 p 1) regularization schemes.     

一种求解弹性l_2-l_q正则化问题的算法

给出了一种求解弹性l_{2}-l_{q}正则化问题的迭代重新加权l_{1}极小化算法, 并证明了由该算法产生的迭代序列是有界且渐进正则的. 对于任何有理数q\in(0,1), 基于一个代数的方法, 进一步证明了迭代重新加权l_{1}极小化算法收敛到弹性l_{2}-l_{q}(0相似文献   

L1 regularization method in electrical impedance tomography by using the L1-curve (Pareto frontier curve)

Electrical impedance tomography (EIT), as an inverse problem, aims to calculate the internal conductivity distribution at the interior of an object from current-voltage measurements on its boundary. Many inverse problems are ill-posed, since the measurement data are limited and imperfect. To overcome ill-posedness in EIT, two main types of regularization techniques are widely used. One is categorized as the projection methods, such as truncated singular value decomposition (SVD or TSVD). The other categorized as penalty methods, such as Tikhonov regularization, and total variation methods. For both of these methods, a good regularization parameter should yield a fair balance between the perturbation error and regularized solution. In this paper a new method combining the least absolute shrinkage and selection operator (LASSO) and the basis pursuit denoising (BPDN) is introduced for EIT. For choosing the optimum regularization we use the L1-curve (Pareto frontier curve) which is similar to the L-curve used in optimising L2-norm problems. In the L1-curve we use the L1-norm of the solution instead of the L2 norm. The results are compared with the TSVD regularization method where the best regularization parameters are selected by observing the Picard condition and minimizing generalized cross validation (GCV) function. We show that this method yields a good regularization parameter corresponding to a regularized solution. Also, in situations where little is known about the noise level σ, it is also useful to visualize the L1-curve in order to understand the trade-offs between the norms of the residual and the solution. This method gives us a means to control the sparsity and filtering of the ill-posed EIT problem. Tracing this curve for the optimum solution can decrease the number of iterations by three times in comparison with using LASSO or BPDN separately.     

退化弱(L1,L2)-BLD映射的正则性

本文给出空间退化的弱(L1,L2)-BLD映射的定义.利用Hodge分解,弱逆Holder不等式等工具,证明了其正则性结果对任意满足0＜L2lnl/2l2n+l×100n2[23l/2.(24l+n+1)](l-q1)＜1的q1,都存在可积指数p1=p1(n,l,q1,L1,L2)＞l,使得对任意退化的弱(L1,L2)-BLD映射f∈W1,q1 loc(Ω,Rn),都有f∈W1,p1 loc (Ω,Rn),即f为通常意义下的退化的(L1,L2)-BLD映射.     

ENHANCED BLOCK-SPARSE SIGNAL RECOVERY PERFORMANCE VIA TRUNCATED e2/e1-2 MINIMIZATION

In this paper, we investigate truncated $ℓ_2/ℓ_{1−2}$ minimization and its associated alternating direction method of multipliers (ADMM) algorithm for recovering the block sparse signals. Based on the block restricted isometry property (Block-RIP), a theoretical analysis is presented to guarantee the validity of proposed method. Our theoretical results not only show a less error upper bound, but also promote the former recovery condition of truncated ℓ₁₋₂ method for sparse signal recovery. Besides, the algorithm has been compared with some state-of-the-art algorithms and numerical experiments have shown excellent performances on recovering the block sparse signals.     

图的L(3,2,1)-标号

无向图G的L(3,2,1)-标号是指从顶点集V(G)到非负整数集Z*的一个映射,满足:对i=1,2,3,只要dG(x,y)=i,则f(x)-f(y)|≥4-i.若一个L(3,2,1)-标号中的所有像元素都不超过整数k,则称之为k-L(3,2,1)-标号.图G的L(3,2,1)-标号数,记作3λ(G),是使得图G存在k-L(3,2,1)-标号的最小整数k.文中给出了路、圈、树等特殊图的L(3,2,1)-标号数,并给出了一般图的L(3,2,1)-标号数的一个上界.     

图的L(2,1)标号与移动通讯频率分配问题

图G的L（2，1）标号是一个从顶点集V（G）到非负整数集的函数f(x)，使得若d(x,y)=1，则|f(x)-f(y)|≥2；若d(x,y)=2，则|f(x)-f(y)|≥1。移动通讯频率分配问题可以转化为图的L（2，1）标号问题。本文首先给出平面格子图的L（2，1）标号，然后通过平面格子图及相关图的L（2，1）标号得到平面近正六边形剖分图的L（2，1）面标号，从而解决了移动通讯的频率分配问题。     

1/2-Transitive Graphs of Order 3p

A graph X is called vertex-transitive, edge-transitive, or arc-transitive, if the automorphism group of X acts transitively on the set of vertices, edges, or arcs of X, respectively. X is said to be 1/2-transitive, if it is vertex-transitive, edge-transitive, but not arc-transitive.In this paper we determine all 1/2-transitive graphs with 3p vertices, where p is an odd prime. (See Theorem 3.4.)