In applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach. k -SVD is a generalization of the k -means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and ...

We present a new method – the K-SVD algorithm – generalizing the K-Means clustering process. K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary, and a process of updating the dictionary atoms to better fit the data.

K. Engan, S. O. Aase, and J. H. Hakon-Husoy, “Method of Optimal Directions for Frame Design,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999, vol. 5, pp. 2443-2446.

这篇论文基本上把K-SVD算法的实现原理用中文转述了一遍，如果想了解具体细节可以参考原论文和上面这篇中文文章，接下来我将用更直觉地方式来介绍一下K-SVD的算法流程：

2006年10月16日 · We present a new method-the K-SVD algorithm-generalizing the K-means clustering process. K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of …

2017年11月9日 · K-SVD算法算法简介 K-SVD可以看做K-means的一种泛化形式 (由K-means扩展而来)，K-means算法中每个信号量只能用一个原子来近似表示，而K-SVD中每个信号是用多个原子的线性组合来表示的。 K-SVD通过构建字典来对数据进行稀疏表示，经常用于图像压缩、编码、分 …

ions, thereby accelerating convergence. The K -SVD algorithm is flexible and can work with any pursuit method (e.g., b. sis pur-suit, FOCUSS, or matching pursuit). We analyze this algorithm and demonstrate its results both on synthetic tests and in applica-tions on real image data. Index Terms—Atom d.

In this paper we propose a novel algorithm the K-SVD algorithm generalizing the K-Means clustering process, for adapting dictionaries in order to achieve sparse signal representations. We analyze this algorithm and demonstrate its results on both synthetic tests and in applications on real data. 1. INTRODUCTION.

2019年9月28日 · More specifically, we propose an end-to-end deep architecture with the exact K-SVD computational path, and train it for optimized denoising. Our work shows how to overcome difficulties arising in turning the K-SVD scheme into a differentiable, and thus learnable, machine.

The synthesis-based sparse representation model for signals has drawn a considerable interest in the past decade. Such a model assumes that the signal of intere.