Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.

中文翻译：

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迭代式深度邻居：涉及输入数据点及其邻居的深度学习模型。

深度学习模型（例如深度卷积神经网络和深度长期短期记忆模型）已在许多模式分类应用程序中取得了超过具有手工特征的影子机器学习模型的巨大成功。主要原因是深度学习模型能够通过多层神经元自动从海量数据中提取分层特征。但是，在许多其他情况下，由于模型输入的限制，现有的深度学习模型仍然无法获得令人满意的结果。现有的深度学习模型仅采用输入点的数据实例，而完全忽略数据集中的其他数据点，这可能为给定输入的分类提供关键的见解。为了克服这一差距，在本文中，我们显示，除了输入数据点本身之外，相邻数据点还可以显着提高深度学习模型的性能，并设计一种新颖的深度学习模型，该模型将输入点的数据实例及其邻居的分类响应作为输入。此外，我们开发了一种迭代算法，该算法根据深度学习模型输出的深度表示和深度学习模型的参数交替更新数据点的邻居。所提出的算法称为“迭代深度邻域（IDN）”，在图像分类，文本情感分析，房地产价格趋势预测等任务方面，它显示了优于最新深度学习模型的优势。