Introduction to Sparsity in Signal Processing.
Recent research of sparse signal representation has aimed at learning discriminative sparse models instead of purely reconstructive ones for classification tasks, such as sparse representation.
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Furthermore, frameworks for signal classification using sparse and overcomplete representations have been developed. Data-dependent representations using learned dictionaries have been significant in applications such as feature extraction and denoising. In this paper, our goal is to perform pattern classification in a domain referred to as the data representation domain, where data from.
Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on. This paper investigates the problem of robust face classification when a test sample has missing values.
Sparse representation based classification (SRC) has achieved state-of-the-art results on face recognition. It is hence desired to extend its power to a broader range of classification tasks in pattern recognition. SRC first encodes a query sample as a linear combination of a few atoms from a predefined dictionary. It then identifies the label by evaluating which class results in the minimum.
Sparse signal approximation has gained popularity over the last decade. The sparse approximation model suggests that a natural signal could be compactly approximated by only a few atoms out of a properly given dictionary, where the weights associated with the dictionary atoms are called the sparse codes. Proven to be both robust to noise and scalable to high-dimensional data, sparse codes are.
At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain.