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首页> 外文会议>International Conference on Neural Information Processing >Learning Deep Neural Network Based Kernel Functions for Small Sample Size Classification
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Learning Deep Neural Network Based Kernel Functions for Small Sample Size Classification

机译：学习基于神经网络的基于神经网络的内核功能，用于小样本大小分类

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摘要

Kernel learning is to learn a kernel function based on the set of all sample pairs from training data. Even for small sample size classification tasks, the set size is mostly large enough to make a complex kernel that holds lots of parameters being well optimized. Hence, the complex kernel can be helpful in improving classification performance via providing more meaningful feature representation in kernel induced feature space. In this paper, we propose to embed a deep neural network (DNN) into kernel functions, taking its output as kernel parameter to adjust the feature representations adaptively. Two kind of DNN based kernels are defined, and both of them are proved to satisfy the Mercer theorem. Considering the connection between kernel and classifier, we optimize the proposed DNN based kernels by exploiting the GMKL alternating optimization framework. A stochastic gradient descent (SGD) based algorithm is also proposed, which still implements alternating optimization in each iteration. Furthermore, an incremental batch size method is given to reduce gradient noise gradually in optimization process. Experimental results show that our method performed better than the typical methods.
机译：内核学习是根据训练数据的所有样本对的集合学习内核功能。即使对于小型示例大小分类任务，SET大小也足够大，可以制作一个复杂的内核，其中包含大量优化的参数。因此，复杂的内核通过在内核引起的特征空间中提供更有意义的特征表示，复杂的内核可以有助于提高分类性能。在本文中，我们建议将深度神经网络（DNN）嵌入内核函数，将其输出作为内核参数，以自适应地调整特征表示。定义了两种基于DNN的内核，并证明了两者都能满足Mercer定理。考虑内核和分类器之间的连接，我们通过利用GMKL交替优化框架来优化基于DNN基础的内核。还提出了一种随机梯度下降（SGD）的算法，其仍然在每次迭代中实现交替的优化。此外，给出了增量批量尺寸方法以在优化过程中逐渐降低梯度噪声。实验结果表明，我们的方法比典型方法更好。

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