In applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach.

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 …

K-SVD:Step2-DictionaryUpdate Goal: To find (d k, x k) such that minimize d k, x k k E k − d k x kk2 F. (18) Not quite! We also need to preserve sparsity of x k. Okay. Then, let us restrict ourselves to the existing non-zeros of x k. Define ω k = {i : x k[i] 6= 0 }, (19) and Ω k be an n×|ω k| matrix representing the sampling operator ...

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 -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation Michal Aharon, Michael Elad, and Alfred Bruckstein Abstract—In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcom-plete dictionary that contains prototype signal-atoms, signals are

2006年12月1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...

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10月25日 · K-singular value decomposition (K-SVD) is a frequently used dictionary learning (DL) algorithm that iteratively works between sparse coding and dictionary updating. The sparse coding process generates sparse coefficients for each training sample, and the sparse coefficients induce clustering features.

2018年9月22日 · A novel criterion to the well-known dictionary learning technique, K-SVD, is proposed. The approach exploits the L1-norm as the cost function for the dictionary update stage of K-SVD in order to provide robustness against impulsive noise and outlier input samples.

Our goal is to learn the analysis dictionary from a set of signal examples, and the approach taken is parallel and similar to the one adopted by the K-SVD algorithm that serves the corresponding problem in the synthesis model.