Sparse representation
113 Followers
Recent papers in Sparse representation
We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their... more
Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific sub-area of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM)... more
The problem addressed is source localization from time differences of arrival (TDOA). This problem is also referred to as hyperbolic localization and it is non-convex in general. Traditional solutions proposed in the literature have... more
Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Due to this advantage, pixel-level... more
Facial expression recognition is to determine the emotional state of the face regardless of its identity. Most of the existing datasets for facial expressions are captured in a visible light spectrum. However, the visible light (VIS) can... more
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component... more
In this paper, wavelets and fuzzy support vector machines are used to automated detect and classify power quality (PQ) disturbances. Electric power quality is an aspect of power engineering that has been with us since the inception of... more
Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel. This paper examines the applicability of a recently developed algorithm called graph regularized... more
Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper,... more
Facial expression recognition is to determine the emotional state of the face regardless of its identity. Most of the existing datasets for facial expressions are captured in a visible light spectrum. However, the visible light (VIS) can... more
Convolutional neural networks (CNNs) have taken the spotlight in a variety of machine learning applications. To reach the desired performance, CNNs have become increasingly deeper and larger which goes along with a tremendous amount of... more
The use of exemplar-based methods, such as support vector machines (SVMs), k-nearest neighbors (kNNs) and sparse representations (SRs), in speech recognition has thus far been limited. Exemplar-based techniques utilize information about... more