Abstract not found

Sparse coding — that is, modelling data vectors as sparselinearcombinationsofbasiselements—is widelyusedinmachinelearning,neuroscience, signalprocessing,andstatistics. Thispaperfocusesonlearningthebasisset, alsocalleddictionary,toadaptittospecificdata,anapproach thathasrecentlyproventobeveryeffectivefor signalreconstructionandclassificationin theaudioandimageprocessingdomains. This paper proposesanewonlineoptimizationalgorithm fordictionarylearning, basedonstochasticapproximations, whichscalesupgracefullytolarge datasetswithmillionsoftrainingsamples. A proofofconvergenceispresented, along with experimentswithnaturalimagesdemonstrating thatitleadstofasterperformanceandbetter dictionariesthanclassicalbatchalgorithmsforboth

Abstract—Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established

In this paper, we present dictionary learning methods for sparse and redundant signal representations in high dimensional feature space. Using the kernel method, we describe how the well-known dictionary learning approaches such as the method of optimal directions and K-SVD can be made nonlinear

Abstract. Existing dictionary learning algorithms rely heavily on the assumption that the data points are vectors in some Euclidean space R d, and the dictionary is learned from the input data using only the vector space structure of R d. However, in many applications, features and data points

Abstract. Many recent efforts have shown the effectiveness of dictionary learning methods in solving several computer vision problems. However, when designing dictionaries, training and testing domains may be different, due to different view points and illumination conditions. In this paper, we

Abstract — Dictionary learning has been widely used in many image processing tasks. In most of these methods, the number of basis vectors is either set by experience or coarsely evaluated empirically. In this paper, we propose a new scale adaptive dictionary learning framework, which jointly

Online dictionary learning is particularly useful for processing large-scale and dynamic data in computer vision. It, however, faces the major difficulty to incorporate robust functions, rather than the square data fitting term, to handle outliers in training data. In this paper, we propose a new

Abstract. Sparse coding plays a key role in high dimensional data anal-ysis. One critical challenge of sparse coding is to design a dictionary that is both adaptive to the training data and generalizable to unseen data of same type. In this paper, we propose a novel dictionary learning method

constrained sparse dictionary learning