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Fast Approximation for Sparse Coding with Applications to Object Recognition

机译：用于对象识别的稀疏编码的快速近似

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摘要

Sparse Coding (SC) has been widely studied and shown its superiority in the fields of signal processing, statistics, and machine learning. However, due to the high computational cost of the optimization algorithms required to compute the sparse feature, the applicability of SC to real-time object recognition tasks is limited. Many deep neural networks have been constructed to low fast estimate the sparse feature with the help of a large number of training samples, which is not suitable for small-scale datasets. Therefore, this work presents a simple and efficient fast approximation method for SC, in which a special single-hidden-layer neural network (SLNNs) is constructed to perform the approximation task, and the optimal sparse features of training samples exactly computed by sparse coding algorithm are used as ground truth to train the SLNNs. After training, the proposed SLNNs can quickly estimate sparse features for testing samples. Ten benchmark data sets taken from UCI databases and two face image datasets are used for experiment, and the low root mean square error (RMSE) results between the approximated sparse features and the optimal ones have verified the approximation performance of this proposed method. Furthermore, the recognition results demonstrate that the proposed method can effectively reduce the computational time of testing process while maintaining the recognition performance, and outperforms several state-of-the-art fast approximation sparse coding methods, as well as the exact sparse coding algorithms.
机译：稀疏编码（SC）已被广泛研究并显示其在信号处理，统计和机器学习领域的优越性。然而，由于计算稀疏特征所需的优化算法的高计算成本，SC对实时对象识别任务的适用性是有限的。在大量训练样本的帮助下，许多深度神经网络已经构建为低快速估计稀疏特征，这不适用于小型数据集。因此，该工作提出了一种简单有效的SC的快速近似方法，其中构建了特殊的单隐层神经网络（SLNNS）以执行近似任务，并且通过稀疏编码恰好计算的训练样本的最佳稀疏特征算法用作地面真理来训练SLNN。在培训之后，所提出的SLNN可以快速估计测试样品的稀疏功能。从UCI数据库和两个面部图像数据集采取的十个基准数据集用于实验，并且在近似稀疏特征和最佳稀疏特征和最佳的低根均方误差（RMSE）验证了该方法的近似性能。此外，识别结果表明，所提出的方法可以有效地降低测试过程的计算时间，同时保持识别性能，并且优于几种最先进的快速近似稀疏编码方法，以及精确的稀疏编码算法。

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