K-L

K-L的面部识别源代码采用MATLAB实现，支持多种图像格式的识别。随着技术的进步，这一源代码在图像识别领域具有广泛的适用性。

使用Matlab实现了K-L图像变换，用于人脸识别的应用。该方法通过降维和特征提取，有效地识别人脸图像中的关键特征。

K-means algorithm has long been a staple in machine learning and data mining fields, primarily for its effectiveness in clustering large-scale datasets. However, traditional K-means clustering doesn't inherently distinguish the varying discriminative power of features in data. To address this, the paper proposes an innovative clustering framework incorporating L2-norm regularization on feature weights, thereby enhancing clustering outcomes. This new approach builds on the Weighted K-means (W-K-means) algorithm by applying L2-norm regularization to feature weights, effectively balancing feature importance. For numerical datasets, this framework introduces the l2-Wkmeans algorithm, which uses conventional means as cluster centers. For categorical datasets, two variations—l2-NOF (Non-numeric features based on different smoothing modes) and l2-NDM (Non-numeric features based on distance metrics)—are proposed. The essence of these methods lies in their updated clustering objective function and derived update rules for cluster centers, membership matrices, and feature weights. Extensive experiments demonstrate the superior performance of the proposed algorithms on both numerical and categorical datasets. These methods exhibit advantages such as improved clustering accuracy, robustness to noisy data, and adaptability to high-dimensional data environments. This signifies that incorporating L2-norm regularization for feature weighting substantially enhances the clustering quality of K-means, especially for complex, high-dimensional datasets. Additionally, the study discusses the impact of regularization parameters on clustering performance, offering practical insights for tuning these parameters to optimize clustering results. This guidance allows users to select the appropriate regularization intensity based on task-specific and data-related characteristics. The research provides a fresh perspective on improving the K-means clustering algorithm by emphasizing feature importance through L2-norm regularization, enhancing both clustering power and generalizability. This method is valuable for large-scale datasets and scenarios that require nuanced feature differentiation, representing a significant step forward in clustering quality and advancing related research fields.

MATLAB源码用于严格分层线性规划中L2范数的加权方法存储库，适用于机器人控制。使用L1范数作为正则化步骤可以实现对机器人系统的稀疏或简约控制。此存储库包含提交给IEEE RA-L/ICRA审查的论文的源代码，正在审核中。即将发布的文档提升代码的可读性。实验视频展示了双臂控制中WLP-L1算法和WLP-L2算法的效果，以及对偶技巧的重构。对偶技巧的源代码可在对偶技巧文件夹中找到，用于将字典线性程序重新表述为单目标线性程序。要运行此代码，需要安装MATLAB和Yalmip工具箱，并建议安装免费学术许可证的Gurobi以重现报告的计算性能。另外，还提供了用于分层二次规划的未记录的对偶技巧的实现。该代码在Ubuntu 18.04LTS上测试，并包括Python3.7或更高版本的依赖项CasADi和PyBullet，用于任务功能的自动区分和优化求解器接口，以及模拟和可视化机器人运动。

This document provides various examples of basic compressed sensing using the MATLAB function linprog. The following examples demonstrate how to apply compressed sensing techniques to different types of signals: ECG Signal Compression K-sparse Signal Recovery Audio Signal Compression Encrypted Data Recovery Image Compression via L1 minimization Each section includes practical code examples, with step-by-step explanations to help you understand how to implement compressed sensing in different applications.

L1产生候选集C2： 项集 ｛I1，I2｝｛I1，I3｝｛I1，I4｝｛I1，I5｝｛I2，I3｝｛I2，I4｝｛I2，I5｝｛I3，I4｝｛I3，I5｝｛I4，I5｝

l-曲线 MATLAB 代码与 cgDNA：DNA的序列依赖性粗粒模型的实现方法，通过模拟 DNA 序列的特征，深入分析其结构和功能。该模型不仅能够有效展示 DNA 的复杂性，还提供了对序列变化的灵敏响应，便于科研人员进行进一步研究和探索。

本 MATLAB 工具将 xyz 坐标文件转换为适用于激光直接墨水书写（L-DIW）的 xyz-theta 系列 GCode，可旋转打印样本。

Matlab 代码中的 L 通常表示归纳器或电感。

ADC合集L#专用！