Learning adaptive dictionaries for sparsecoding has been the focus of latest research as it provides a promising way to maximize the efficiency of sparse *** particular, learning discriminative dictionaries rather th...
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Learning adaptive dictionaries for sparsecoding has been the focus of latest research as it provides a promising way to maximize the efficiency of sparse *** particular, learning discriminative dictionaries rather than reconstructive ones has demonstrated significantly improved performance in pattern *** this paper, a powerful method is proposed for discriminative dictionary *** the dictionary learning process, we enhance the discriminability of sparse codes by promoting hierarchical group sparsity and reducing linear prediction error on sparse *** the employment of joint within-class collaborative hierarchical sparsity, our method is able to learn adaptive dictionaries from labeled data for classification, which encourage coefficients to be sparse at both group level and singleton level and thus enforce the separability of sparse *** from joint dictionary and classifier learning, the discriminability of sparse codes is further *** efficient alternating iterative scheme is presented to solve the proposed *** applied our method to face recognition, object recognition and scene *** results have demonstrated the excellent performance of our method in comparison with existing discriminative dictionary learning approaches.
We propose a method to determine unintelligible words based on the textual context of the word determined. As there can be many different possibilities for the word, a robust, large-scale method is needed. The large s...
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We propose a method to determine unintelligible words based on the textual context of the word determined. As there can be many different possibilities for the word, a robust, large-scale method is needed. The large scale makes the problem sensitive to spurious similarities of contexts: when the contexts of two, different words are similar. To reduce this effect, we induce structured sparsity on the words by formulating the task as a group Lasso problem. We compare this formulation to a k-nearest neighbor and a support vector machine based approach, and find that group Lasso outperforms both by a large margin. We achieve up to 75% of accuracy when determining the word from among 1000 words both on the Brown corpus and on the British National Corpus. Unintelligible words are often the result of errors in Optical Character Recognition (OCR) algorithms. As the proposed method utilizes information independent from information used in OCR, we expect that a combined approach could be very successful, as OCR and the proposed method complement each other.
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