自组织映射

Synopsis

This operator performs a dimensionality reduction of the given ExampleSet based on a self-organizing map (SOM). The user can specify the required number of dimensions.

Description

A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network 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. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by Teuvo Kohonen, and is sometimes called a Kohonen map.

Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. Mapping automatically classifies a new input vector. A self-organizing map consists of components called nodes or neurons. Associated with each node is a weight vector of the same dimension as the input data vectors and a position in the map space. The usual arrangement of nodes is a regular spacing in a hexagonal or rectangular grid. The self-organizing map describes a mapping from a higher dimensional input space to a lower dimensional map space. The procedure for placing a vector from data space onto the map is to first find the node with the closest weight vector to the vector taken from data space. Once the closest node is located it is assigned the values from the vector taken from the data space.

While it is typical to consider this type of network structure as related to feed-forward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation.

Input

example set input

This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input. It is essential that meta data should be attached with the data for the input because attributes are specified in their meta data. The Retrieve operator provides meta data along with the data. Please note that this operator cannot handle nominal attributes; it works on numerical attributes.

Output

example set output

The dimensionality reduction of the given ExampleSet is performed based on a self-organizing map and the resultant ExampleSet is delivered through this port.

original

The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.

preprocessing model

This port delivers the preprocessing model, which has information regarding the parameters of this operator in the current process.

Parameters

Return preprocessing model

This parameter indicates if the preprocessing model should be returned.

Number of dimensions

This parameter specifies the number of dimensions to keep i.e. the number of attributes of the resultant ExampleSet.

Net size

This parameter specifies the size of the SOM net, by setting the length of every edge of the net. In total, there will benet sizeto the power ofnumber of dimensionsnodes in the net.

Training rounds

This parameter specifies the number of training rounds.

Learning rate start

This parameter specifies the strength of an adaption in the first round. The strength will decrease every round until it reaches thelearning rate endin the last round.

Learning rate end

This parameter specifies the strength of an adaption in the last round. The strength will decrease to this value in last round, beginning withlearning rate startin the first round.

Adaption radius start

This parameter specifies the radius of the sphere around a stimulus in the first round. This radius decreases every round, starting byadaption radius startin the first round, toadaption radius endin the last round.

Adaption radius end

This parameter specifies the radius of the sphere around a stimulus in the last round. This radius decreases every round, starting byadaption radius startin the first round, toadaption radius endin the last round.