Robust DCD-Based Recursive Adaptive Algorithms

基于逆幂方法和二分坐标下降迭代的递归总最小二乘算法

Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations

本文提出一种基于逆幂方法和二分坐标下降迭代的递归总最小二乘算法，相比传统方法，计算复杂度更低且具有渐近无偏性和稳定性，同时推导了遗忘因子下界和稳态均方偏差理论值。

Regularized Kernel Recursive Least Square Algoirthm

In most adaptive signal processing applications, system linearity is assumed and adaptive linear filters are thus used. The traditional class of supervised adaptive filters rely on error-correction learning for their adaptive capability. The kernel method is a powerful nonparametric modeling tool for pattern analysis and statistical signal processing. Through a nonlinear mapping, kernel methods transform the data into a set of points in a Reproducing Kernel Hilbert Space. KRLS achieves high accuracy and has fast convergence rate in stationary scenario. However the good performance is obtained …

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods

We present AI-SARAH, a practical variant of SARAH. As a variant of SARAH, this algorithm employs the stochastic recursive gradient yet adjusts step-size based on local geometry. AI-SARAH implicitly computes step-size and efficiently estimates local Lipschitz smoothness of stochastic functions. It is fully adaptive, tune-free, straightforward to implement, and computationally efficient. We provide technical insight and intuitive illustrations on its design and convergence. We conduct extensive empirical analysis and demonstrate its strong performance compared with its classical counterparts and…

FRAME：前向递归自适应模型提取——一种高级特征选择技术

"FRAME: Forward Recursive Adaptive Model Extraction-A Technique for Advance Feature Selection"

FRAME通过结合前向选择和递归特征消除，提升特征选择的准确性与效率，适用于需要可解释和准确预测的应用。

Universal Transformer Hawkes Process with Adaptive Recursive Iteration

Asynchronous events sequences are widely distributed in the natural world and human activities, such as earthquakes records, users activities in social media and so on. How to distill the information from these seemingly disorganized data is a persistent topic that researchers focus on. The one of the most useful model is the point process model, and on the basis, the researchers obtain many noticeable results. Moreover, in recent years, point process models on the foundation of neural networks, especially recurrent neural networks (RNN) are proposed and compare with the traditional models, th…

Study of Robust Distributed Diffusion RLS Algorithms with Side Information for Adaptive Networks

This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. Th…