کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969652 | 1449982 | 2017 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Locality-constrained max-margin sparse coding
ترجمه فارسی عنوان
برنامه نویسی پراکنده حداکثر حاشیه محدوده محلی
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کلمات کلیدی
محل سکونت، رمز گشایی، حداکثر حاشیه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
This work devises a locality-constrained max-margin sparse coding (LC-MMSC) framework, which jointly considers reconstruction loss and hinge loss simultaneously. Traditional sparse coding algorithms use â1 constraint to force the representation to be sparse, leading to computational expensive process to optimize the objective function. This work uses locality constraint in the framework to preserve information of data locality and avoid the optimization of â1. The obtained representation can achieve the goal of data locality and sparsity. Additionally, this work optimizes coefï¬cients, dictionaries and classiï¬cation parameters simultaneously, and uses block coordinate descent to learn all the components of the proposed model. This work uses semi-supervised learning approach in the proposed framework, and the goal is to use both labeled data and unlabeled data to achieve accurate classiï¬cation performance and improve the generalization of the model. We provide theoretical analysis on the convergence of the proposed LC-MMSC algorithm based on Zangwill's global convergence theorem. This work conducts experiments on three real datasets, including Extended YaleB dataset, AR face dataset and Caltech101 dataset. The experimental results indicate that the proposed algorithm outperforms other comparison algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 65, May 2017, Pages 285-295
Journal: Pattern Recognition - Volume 65, May 2017, Pages 285-295
نویسندگان
Wen-Hoar Hsaio, Chien-Liang Liu, Wei-Liang Wu,