Abstract

Feature representation is generally applied to reducing the dimensions of high-dimensional data to accelerate the process of data handling and enhance the performance of pattern recognition. However, the dimensionality of data nowadays appears to be a rapidly increasing trend. Existing unsupervised feature representation methods are susceptible to the rapidly increasing dimensionality of data, which may result in learning a meaningless feature that in turn affect their performance in other applications. In this paper, an unsupervised adversarial auto-encoder network is studied. This network is a probability model that combines generative adversarial networks and variational auto-encoder to perform variational inference and aims to generate reconstructed data similar to original data as much as possible. Due to its adversarial training, this model is relatively robust in feature learning compared with other methods. First, the architecture and training strategy of adversarial auto-encoder are presented. We attempt to learn a discriminative feature representation for high-dimensional image data via adversarial auto-encoder and take its advantage into image clustering, which has become a difficult computer vision task recently. Then amounts of comparative experiments are carried out. The comparison contains eight feature representation methods and two recently proposed deep clustering methods performed on eight different publicly available image data sets. Finally, to evaluate their performance, we utilize a K-means clustering on the low-dimensional feature learned from each feature representation algorithm, and select three evaluation metrics including clustering accuracy, adjusted rand index and normalized mutual information, to provide a comparison. Comprehensive experiments prove the usefulness of the learned discriminative feature via adversarial auto-encoder in the tested data sets.

中文翻译：

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通过对抗性自动编码器的无监督区分特征表示

摘要

特征表示通常用于减少高维数据的维数，以加速数据处理过程并增强模式识别的性能。但是，如今数据的维数似乎是一个快速增长的趋势。现有的无监督特征表示方法容易受到数据维数快速增加的影响，这可能导致学习无意义的特征，进而影响其在其他应用程序中的性能。本文研究了一种无监督的对抗自动编码器网络。该网络是一个概率模型，该模型结合了生成对抗网络和变分自动编码器以执行变分推理，并旨在生成与原始数据尽可能相似的重构数据。由于进行了对抗训练，与其他方法相比，该模型在特征学习方面相对强大。首先，提出了对抗性自动编码器的体系结构和训练策略。我们试图通过对抗性自动编码器来学习高维图像数据的判别特征表示，并将其优势用于图像聚类，这已成为近来一项艰巨的计算机视觉任务。然后进行大量的对比实验。比较包含八种特征表示方法和两种最近提出的针对八种不同的公共可用图像数据集执行的深度聚类方法。最后，为了评估其效果，我们利用 我们试图通过对抗性自动编码器来学习高维图像数据的判别特征表示，并将其优势用于图像聚类，这已成为近来一项艰巨的计算机视觉任务。然后进行大量的对比实验。比较包含八种特征表示方法和两种最近提出的针对八种不同的公共可用图像数据集执行的深度聚类方法。最后，为了评估其效果，我们利用 我们试图通过对抗性自动编码器来学习高维图像数据的判别特征表示，并将其优势用于图像聚类，这已成为近来一项艰巨的计算机视觉任务。然后进行大量的对比实验。比较包含八种特征表示方法和两种最近提出的针对八种不同的公共可用图像数据集执行的深度聚类方法。最后，为了评估其效果，我们利用 该比较包含八种特征表示方法和两种最近提出的针对八种不同的公共可用图像数据集执行的深度聚类方法。最后，为了评估其效果，我们利用 比较包含八种特征表示方法和两种最近提出的针对八种不同的公共可用图像数据集执行的深度聚类方法。最后，为了评估其效果，我们利用K均值表示从每个特征表示算法中学习的低维特征聚类，并选择三个评估指标（包括聚类精度，调整的兰德指数和归一化的互信息）进行比较。全面的实验通过测试数据集中的对抗性自动编码器证明了所学习的区分功能的有用性。