摘要翻译：
提出了一种基于多层镜像神经网络和Forgy聚类算法的无监督学习方法。多层镜像神经网络是一种神经网络，它可以用广义的数据输入（不同类别的图像模式）进行非线性降维，并用Forgy的算法将得到的低维码用于无监督模式分类。通过调整非线性激活函数（修正的sigmoidal函数）并将权值和偏差项初始化为小的随机值，启动了输入模式的镜像。在训练中，权重和偏置项以这样一种方式改变，即通过反向传播误差在输出处再现所呈现的输入。镜像神经网络能够在很大程度上减少输入向量（约为原始尺寸的1/30)，并且能够从减少的代码单元重构输出层的输入模式。从该网络中提取的特征集合（中心隐层的输出）被馈送到Forgy的算法中，该算法将输入数据模式分类为可区分的类别。在Forgy算法的实现中，初始种子点的选择是以这样一种方式进行的，即它们足够远，可以完美地分组到不同的类别中。为此，本文提出并论证了一种新的无监督学习方法。将该方法应用于不同图像模式的分类，取得了良好的效果。
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英文标题：
《Automatic Pattern Classification by Unsupervised Learning Using
  Dimensionality Reduction of Data with Mirroring Neural Networks》
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作者：
Dasika Ratna Deepthi, G.R.Aditya Krishna and K. Eswaran
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
  This paper proposes an unsupervised learning technique by using Multi-layer Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer Mirroring Neural Network is a neural network that can be trained with generalized data inputs (different categories of image patterns) to perform non-linear dimensionality reduction and the resultant low-dimensional code is used for unsupervised pattern classification using Forgy's algorithm. By adapting the non-linear activation function (modified sigmoidal function) and initializing the weights and bias terms to small random values, mirroring of the input pattern is initiated. In training, the weights and bias terms are changed in such a way that the input presented is reproduced at the output by back propagating the error. The mirroring neural network is capable of reducing the input vector to a great degree (approximately 1/30th the original size) and also able to reconstruct the input pattern at the output layer from this reduced code units. The feature set (output of central hidden layer) extracted from this network is fed to Forgy's algorithm, which classify input data patterns into distinguishable classes. In the implementation of Forgy's algorithm, initial seed points are selected in such a way that they are distant enough to be perfectly grouped into different categories. Thus a new method of unsupervised learning is formulated and demonstrated in this paper. This method gave impressive results when applied to classification of different image patterns.
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PDF链接：
https://arxiv.org/pdf/0712.0938