Self - organizing feature maps

Self - organizing feature maps by Teuvo Kohonen Download PDF EPUB FB2

The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real-world by: Since the second edition of this book came out in earlythe number of scientific papers published on the Self-Organizing Map (SOM) has increased from about to some Also, two special workshops dedicated to the SOM have been organized, not to Brand: Springer-Verlag Berlin Heidelberg.

Part of the Springer Series in Information Sciences book series (SSINF, volume 8) Abstract A property which is commonplace in the brain but which has always been ignored in the “learning machines” is a meaningful order of their processing by: Self-organizing feature maps (SOFM), when used appropriately, can exhibit emergent phenomena.

SOFM with only a few neurons limit this ability, therefore emergent feature maps need to have thousands of neurons. The structures of emergent feature maps can be visualized using u-matrix methods.

The Self-Organizing Map was designed for unsupervised learning problems such as feature extraction, visualization and clustering. Some extensions of the approach can label the prepared codebook vectors which can be used for classification. The Self-Organizing Maps: Background, Theories, Extensions and Applications Hujun Yin School of Electrical and Electronic Engineering, The University of Manchester, M60 1QD, UK, @ 1 Introduction For many years, artificial File Size: 1MB.

features inherent to the problem and thus has also been called SOFM, the Self-Organizing Feature Map. 5 Self-Organizing Map (cont.) • Provides a topology preserving mapping from the high dimensional space to map units.

Map units, or neurons, usually form a File Size: KB. A self-organizing map (SOM) is an unsupervised neural network that reduces the input dimensionality in order to represent its distribution as a map. Therefore, SOM forms a map where similar samples are mapped closely together.

The Ultimate Guide to Self Organizing Maps (SOM's) Our input vectors amount to three features, and we have nine output nodes. If you remember the earlier tutorials in this section, we said that SOMs are aimed at reducing the dimensionality of your dataset.

Self-Organizing Feature Map (SOFM or SOM) is a simple algorithm for unsupervised learning. It can be applied to solve vide variety of problems.

It quite good at learning topological structure of the data and it can be used for visualizing deep neural networks. This article explains how SOFM works and shows different applications where it can be used. Since the second edition of this book came out in earlythe number of scientific papers published on the Self-Organizing Map (SOM) has increased from about to some Also, two special workshops dedicated to the SOM have been organized, not to mention numerous SOM sessions in neural network conferences.

In view of this growing interest it was felt desirable to make extensive 4/5(5). Self-organizing map (SOM), sometimes also called a Kohonen map use unsupervised, competitive learning to produce low dimensional, discretized representation of presented high dimensional data, while simultaneously preserving similarity relations between the presented data items.

Such low dimensional representation is called a feature map,File Size: 3MB. Hämäläinen T Parallel implementation of self-organizing maps Self-Organizing neural networks, () Lim T, Loh W and Shih Y () A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms, Machine Language,(), Online publication date: 1-Sep The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature.

It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Self-organizing maps A SOM is a technique to generate topological representations of data in reduced dimensions.

It is one of a number of techniques with such applications, with a better-known alternative being ed on: J   Since the second edition of this book came out in earlythe number of scientific papers published on the Self-Organizing Map (SOM) has increased from about to some Also, two special workshops dedicated to the SOM have been organized, not to mention numerous SOM sessions in neural network : Teuvo Kohonen.

Title: The self-organizing map - Proceedings of the IEEE Author: IEEE Created Date: 2/25/ AMFile Size: 1MB. Self-Organizing Feature Maps; Kohonen Maps 11 2) The feature map is visualized as a "virtual net" in the original pattern space V.

The virtual net is the set of weight vectors wr displayed as points in the pattern space V, together with lines that connect those pairs.

Spherical Self-Organizing Maps: a comprehensive view Paperback – The thesis documents various implementations of the spherical self-organizing feature map spanning from range imagery to multi-dimensional satellite imagery. Other implementations include Author: Archana Sangole.

A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.

An Introduction to Self-Organizing Maps (ii) Cooperation: Similar to “[human] neurons dealing with closely related pieces of information are close together so that they can interact v ia.Self-organizing map SOM有一個很重要的優點為, 將N維(N-dimension)的資料映射(mapping)到2維(2-dimension)的空間上(如圖3所示)並且維持資料中的拓撲(topology)特性.將資料映射到2 維空間時則可以使用視覺化(visualization)的方式呈現, 以方便後續的觀察及分析.3 Kohonen’s self-organizing maps The self-organizing map is a non directed graph G = (N, E) where each vertex n ∈ N is a neuron having a synaptic weight vector wn = (x, y) ∈ ℜ 2, where ℜ2 is the two-dimensional Euclidean space.

Synaptic weight vector corresponds to the vertex location in the plane.