Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity in this field has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on...

Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In...

Sparse representations using over complete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a pre-specified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an itera...

The performance of automatic speech recognition (ASR) often degrades dramatically in noisy environments. In this paper, we present a novel use of dictionary learning approach to normalizing the magnitude modulation spectra of speech features so as to retain more noise-resistant and important acoustic characteristics. To this end, we employ the K-SVD method to create sparse representations for a...

Training and using overcomplete dictionaries has been the subject of many developments in the area of signal processing and sparse representations. The main idea is to train a dictionary that is able to achieve good sparse representations of the items contained in a given dataset. The most popular approach is the K-SVD algorithm and in this paper we study its application to large datasets. The ...

Recent successes in the use of sparse coding for many computer vision applications have triggered the attention towards the problem of how an over-complete dictionary should be learned from data. This is because the quality of a dictionary greatly affects performance in many respects, including computational. While so far the focus has been on learning compact, reconstructive, and discriminativ...

In this paper, we present a novel compression technique for Bidirectional Texture Functions based on a sparse tensor decomposition. We apply the K-SVD algorithm along two different modes of a tensor to decompose it into a small dictionary and two sparse tensors. This representation is very compact, allowing for considerably better compression ratios at the same RMS error than possible with curr...

In recent years there is a growing interest in the study of sparse representation for signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Recent activity in this field concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. In this paper we pr...

We provide new analyses of Lloyd’s algorithm (1982), commonly known as the k-means clustering algorithm. Kumar and Kannan (2010) showed that running k-SVD followed by a constant approximation k-means algorithm, and then Lloyd’s algorithm, will correctly cluster nearly all of the dataset with respect to the optimal clustering, provided the dataset satisfies a deterministic clusterability assumpt...