Please use this identifier to cite or link to this item:
|Title:||A Study on Non-sparse Dictionary Learning for Pattern Classification|
|Publisher:||Institute of Electrical and Electronics Engineers Inc.|
|Abstract:||Dictionary learning (DL) approach has been successfully applied to many pattern classification problems. Sparse property has played an important role in the success of DL-based classification models. However, the sparsity constraints make the learning problem expensive. Recently, there has been an emerged trend in relaxing the sparsity constraints by using L2-norm constraint. The new approach has shown its advantages in both accuracy and classification time. However, the relationship between the quality of the data and the dictionary learning issues that affect the performance of the system has not been investigated. In this paper, we present a comparative study on non-sparse coding dictionary learning for pattern classification. We then propose a dictionary learning model with a non-sparsity constraint on representation coefficients using L2-norm. Our experimental results on three popular benchmark datasets for image classification show that our proposed model can outperform state-of-the-art models and be a promising approach for dictionary learning based classification.|
|Description:||Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015 4 January 2016, Article number 7371815, Pages 371-376|
|Appears in Collections:||Bài báo của ĐHQGHN trong Scopus|
Files in This Item:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.