Order Preserving Sparse Coding | IEEE Journals & Magazine | IEEE Xplore

Sparse coding (SC) has been successfully used in various computer vision applications [1], [2], [3]. Using sparse coding for classification tasks presents two advantages: 1) the method is training-free, if the dictionary is constructed by simply sampling from the training data; and 2) the method provides flexibility in handling dynamic training settings, i.e., new training samples and new class labels. Sparse coding compactly represents an objects as a linear combination of a small number of elements in a dictionary. Data are usually structured. For example, there may exist group structures in the data. To handle such structures, two alternative methods, Elastic Net [4] and group Lasso [5] have been proposed. They favor the selection of a small number of groups of correlated dictionary samples to represent the testing data. Another example is when data are described by several types of features (modalities). In object recognition, we may extract K types of image representations from

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