som

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm using the SOM Toolbox to analyze data collected from a database of cars. My problem is when visualizing the Unified Distance Matrix. Quoting the documentation for som_umat: Compute and return the unified distance matrix of a SOM. For example a case of 5x1 -sized map: m(1) m(2) m(3) m(4) m(5) where m(i) denotes one map unit. The u-matrix is a 9x1 vector: u(1) u(1,2) u(2) u(2,3) u(3) u(3,4) u(4) u(4,5) u(5) where u(i,j) is the distance between map units m(i) and m(j) and u(k) is the mean (or minimum, maximum or median) of the surrounding

There is only one question related to this in stackoverflow, and it is more about which one is better. I just dont really understand the difference. I mean they both work with vectors, which are assigned randomly to clusters, they both work with the centroids of the different clusters in order to determine the winning output node. I mean, where exactly lies the difference? In K-means the nodes (centroids) are independent from each other. The winning node gets the chance to adapt each self and only that. In SOM the nodes (centroids) are placed onto a grid and so each node is consider to have

什么是自组织映射？ 一个特别有趣的无监督系统是基于 竞争性学习 ，其中输出神经元之间竞争激活，结果是在任意时间只有一个神经元被激活。这个激活的神经元被称为 胜者神经元（winner-takes-all neuron） 。这种竞争可以通过在神经元之间具有 横向抑制连接 （负反馈路径）来实现。其结果是神经元被迫对自身进行重新组合，这样的网络我们称之为 自组织映射（Self Organizing Map，SOM） 。 拓扑映射 神经生物学研究表明，不同的感觉输入（运动，视觉，听觉等）以 有序的方式 映射到大脑皮层的相应区域。 这种映射我们称之为 拓扑映射 ，它具有两个重要特性： 在表示或处理的每个阶段，每一条传入的信息都保存在适当的上下文（相邻节点）中 处理密切相关的信息的神经元之间保持密切，以便它们可以通过短突触连接进行交互 我们的兴趣是建立人工的拓扑映射，以神经生物学激励的方式通过自组织进行学习。 我们将遵循 拓扑映射形成的原则 ：“拓扑映射中输出层神经元的空间位置对应于输入空间的特定域或特征”。 建立自组织映射 SOM的主要目标是将任意维度的输入信号模式 转换 为一维或二维离散映射，并以拓扑有序的方式自适应地执行这种变换。 因此，我们通过将神经元放置在一维或二维的网格节点上来建立我们的SOM。更高的尺寸图也是可能的，但不是那么常见。 在竞争性学习过程中，神经元 有选择性地微调

How exactly is an U-matrix constructed in order to visualise a self-organizing-map ? More specifically, suppose that I have an output grid of 3x3 nodes (that have already been trained), how do I construct a U-matrix from this? You can e.g. assume that the neurons (and inputs) have dimension 4. I have found several resources on the web, but they are not clear or they are contradictory. For example, the original paper is full of typos. pater A U-matrix is a visual representation of the distances between neurons in the input data dimension space. Namely you calculate the distance between adjacent

I'm trying to cluster some images depending on the angles between body parts. The features extracted from each image are: angle1 : torso - torso angle2 : torso - upper left arm .. angle10: torso - lower right foot Therefore the input data is a matrix of size 1057x10, where 1057 stands for the number of images, and 10 stands for angles of body parts with torso. Similarly a testSet is 821x10 matrix. I want all the rows in input data to be clustered with 88 clusters. Then I will use these clusters to find which clusters does TestData fall into? In a previous work, I used K-Means clustering which