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Int. J. Wavelets Multiresolution Inf. Process.最新文献

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An aggressive reduction on the complexity of optimization for non-strongly convex objectives 对非强凸目标优化复杂度的积极降低
Pub Date : 2023-05-20 DOI: 10.1142/s0219691323500170
Zhijian Luo, Siyu Chen, Yueen Hou, Yanzeng Gao, Y. Qian
Tremendous efficient optimization methods have been proposed for strongly convex objectives optimization in modern machine learning. For non-strongly convex objectives, a popular approach is to apply a reduction from non-strongly convex to a strongly convex case via regularization techniques. Reduction on objectives with adaptive decrease on regularization tightens the optimal convergence of algorithms to be independent on logarithm factor. However, the initialization of parameter of regularization has a great impact on the performance of the reduction. In this paper, we propose an aggressive reduction to reduce the complexity of optimization for non-strongly convex objectives, and our reduction eliminates the impact of the initialization of parameter on the convergent performances of algorithms. Aggressive reduction not only adaptively decreases the regularization parameter, but also modifies regularization term as the distance between current point and the approximate minimizer. Our aggressive reduction can also shave off the non-optimal logarithm term theoretically, and make the convergent performance of algorithm more compact practically. Experimental results on logistic regression and image deblurring confirm this success in practice.
在现代机器学习中,针对强凸目标优化问题提出了大量高效的优化方法。对于非强凸目标,一种流行的方法是通过正则化技术从非强凸应用到强凸情况。通过正则化的自适应减简使算法的最优收敛性不依赖于对数因子。然而,正则化参数的初始化对约简的性能有很大的影响。在本文中,我们提出了一种主动约简来降低非强凸目标优化的复杂性,并且我们的约简消除了参数初始化对算法收敛性能的影响。主动约简不仅自适应地减小正则化参数,而且将正则化项修改为当前点与近似最小值之间的距离。我们的主动约简还可以从理论上去除非最优对数项,使算法的收敛性能在实践中更加紧凑。逻辑回归和图像去模糊的实验结果在实践中证实了这种方法的成功。
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引用次数: 0
A single image super resolution method based on cross residual network and wavelet transform 基于交叉残差网络和小波变换的单幅图像超分辨方法
Pub Date : 2023-03-08 DOI: 10.1142/s0219691323500169
Kwang-Chol Ri, Chol-Jin Kim
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引用次数: 0
Phase retrieval from short-time fractional Fourier measurements using alternating direction method of multipliers 利用乘法器的交替方向法从短时分数阶傅立叶测量中提取相位
Pub Date : 2023-02-28 DOI: 10.1142/s0219691323500157
Lan Li, Lu Mao, Mingli Jing, Wei Wei, Yang Chen
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引用次数: 0
On the computation of extremal trees of Harmonic index with given edge-vertex domination number 给定边-顶点控制数的调和指数极值树的计算
Pub Date : 2023-02-28 DOI: 10.1142/s0219691323500145
B. Senthilkumar, H. N. Kumar, Y. B. Venkatakrishnan, S. P. Raja
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引用次数: 0
k-Ambiguity function in the framework of offset linear canonical transform 偏移线性正则变换框架下的k-模糊函数
Pub Date : 2023-02-17 DOI: 10.1142/s0219691323500133
M. Younus Bhat, Aamir Hamid Dar
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引用次数: 0
Timber-Prairie Wolf Optimization-Dependent Deep Learning Classifier for Anomaly Detection in Surveillance Videos 基于木材-草原狼优化的监控视频异常检测深度学习分类器
Pub Date : 2023-02-17 DOI: 10.1142/s0219691323500121
B. Gayal, S. Patil
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引用次数: 0
Algorithm of adaptive Fourier decomposition in H2(ℂ+) H2(+)的自适应傅里叶分解算法
Pub Date : 2023-02-10 DOI: 10.1142/s021969132350011x
Weixiong Mai, Tao Qian
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引用次数: 0
DNN-based speech watermarking resistant to desynchronization attacks 基于dnn的抗去同步攻击的语音水印
Pub Date : 2023-02-04 DOI: 10.1142/s0219691323500091
Kosta Pavlovic, Slavko Kovacevic, I. Djurović, Adam Wojciechowski
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引用次数: 1
A Deep Sparse Representation with Random Dictionary for Hyperspectral Image Classification 基于随机字典的高光谱图像分类深度稀疏表示
Pub Date : 2023-02-04 DOI: 10.1142/s0219691323500108
Tian Xia, Chunbo Cheng, Wenjing Cui
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引用次数: 0
Existence of unconditional wavelet bases for Lp-norm over a local fields of positive characteristic 正特征局部域上lp -范数的无条件小波基的存在性
Pub Date : 2023-02-03 DOI: 10.1142/s021969132350008x
Ashish Pathak, Dileep Kumar
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引用次数: 1
期刊
Int. J. Wavelets Multiresolution Inf. Process.
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