Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data

One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.

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Clustering Ensemble Model Based on Self-Organizing Map Network

Computational Intelligence and Neuroscience ◽

10.1155/2020/2971565 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Wenqi Hua ◽

Lingfei Mo

Keyword(s):

Poor Performance ◽

High Dimensional ◽

Ensemble Model ◽

Self Organizing Map ◽

Clustering Ensemble ◽

The Poor ◽

Training Samples ◽

Cascade Structure ◽

Som Network ◽

Self Organizing

This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.

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The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

IEEE Transactions on Neural Networks ◽

10.1109/tnn.2002.804221 ◽

2002 ◽

Vol 13 (6) ◽

pp. 1331-1341 ◽

Cited By ~ 281

Author(s):

A. Rauber ◽

D. Merkl ◽

M. Dittenbach

Keyword(s):

High Dimensional Data ◽

Exploratory Analysis ◽

High Dimensional ◽

Self Organizing Map ◽

Self Organizing

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HDGSOMr: A High Dimensional Growing Self-Organizing Map Using Randomness for Efficient Web and Text Mining

The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05) ◽

10.1109/wi.2005.70 ◽

2005 ◽

Cited By ~ 10

Author(s):

R. Amarasiri ◽

D. Alahakoon ◽

K. Smith ◽

M. Premaratne

Keyword(s):

Text Mining ◽

High Dimensional ◽

Self Organizing Map ◽

Self Organizing

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Randomized Self-Organizing Map

Neural Computation ◽

10.1162/neco_a_01406 ◽

2021 ◽

pp. 1-33

Author(s):

Nicolas P. Rougier ◽

Georgios Is. Detorakis

Keyword(s):

Three Dimensional ◽

Self Organization ◽

Topological Data Analysis ◽

High Dimensional ◽

Dimensional Manifold ◽

Self Organizing Map ◽

Noise Distribution ◽

Data Set ◽

Random Placement ◽

Self Organizing

Abstract We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess random (but controllable) discontinuities that allow for a more flexible self- organization, especially with high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensional tasks, as well as on the MNIST handwritten digits data set and validated using spectral analysis and topological data analysis tools. We also demonstrate the ability of the randomized self-organizing map to gracefully reorganize itself in case of neural lesion and/or neurogenesis.

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A SOM PROJECTION TECHNIQUE WITH THE GROWING STRUCTURE FOR VISUALIZING HIGH-DIMENSIONAL DATA

International Journal of Neural Systems ◽

10.1142/s0129065703001662 ◽

2003 ◽

Vol 13 (05) ◽

pp. 353-365 ◽

Cited By ~ 8

Author(s):

ZHENG WU ◽

GARY G. YEN

Keyword(s):

Projection Method ◽

Cell Structure ◽

High Dimensional Data ◽

Network Size ◽

High Dimensional ◽

Data Sets ◽

Self Organizing Map ◽

Data Set ◽

Growing Cell ◽

Self Organizing

The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional grid structure with a growing self-organizing mechanism. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. In the ordination phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy visualization of the data. The projection method is demonstrated on four different data sets, including a 118 patent data set and a 399 checical abstract data set related to polymer cements, with promising results and a significantly reduced network size.

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