摘要翻译：
在本文中，我们提出了一个镜像神经网络结构来执行非线性维数约简和目标识别使用约简的低维特征向量。除了降维，网络还从降维的低维数据中重构（镜像）原始的高维输入向量。镜像神经网络结构在外层具有较多的处理单元，而在中心层具有最少的处理单元，从而在结构上形成一种收敛-发散的形状。由于该网络能够从最内层的输出（包含输入模式的所有信息）重建原始图像，这些输出可以作为对象特征来对模式进行分类。通过将均方误差从输出层反向传播到输入层，对网络进行训练，使实际输出与输入之间的差异最小化。在成功训练网络后，它可以降低输入向量的维数，并镜像馈送给它的模式。镜像神经网络结构在各种测试模式上都取得了很好的效果。
---
英文标题：
《Dimensionality Reduction and Reconstruction using Mirroring Neural
  Networks and Object Recognition based on Reduced Dimension Characteristic
  Vector》
---
作者：
Dasika Ratna Deepthi, Sujeet Kuchibhotla and K.Eswaran
---
最新提交年份：
2007
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--
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。
--
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。
--

---
英文摘要：
  In this paper, we present a Mirroring Neural Network architecture to perform non-linear dimensionality reduction and Object Recognition using a reduced lowdimensional characteristic vector. In addition to dimensionality reduction, the network also reconstructs (mirrors) the original high-dimensional input vector from the reduced low-dimensional data. The Mirroring Neural Network architecture has more number of processing elements (adalines) in the outer layers and the least number of elements in the central layer to form a converging-diverging shape in its configuration. Since this network is able to reconstruct the original image from the output of the innermost layer (which contains all the information about the input pattern), these outputs can be used as object signature to classify patterns. The network is trained to minimize the discrepancy between actual output and the input by back propagating the mean squared error from the output layer to the input layer. After successfully training the network, it can reduce the dimension of input vectors and mirror the patterns fed to it. The Mirroring Neural Network architecture gave very good results on various test patterns.
---
PDF链接：
https://arxiv.org/pdf/0712.0932