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Anglický jazyk
High Dimensional Data Visualization Using Self Organizing Maps
Autor: Vikas Chaudhary
A Self-organizing map is a non-linear, unsupervised neural network that is used for data clustering and visualization of high-dimensional data. A Self-organizing map uses U-matrix to visualize the high-dimensional data and the distances between neurons on... Viac o knihe
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O knihe
A Self-organizing map is a non-linear, unsupervised neural network that is used for data clustering and visualization of high-dimensional data. A Self-organizing map uses U-matrix to visualize the high-dimensional data and the distances between neurons on the map. However, the structure of clusters and their shapes are often distorted. For better visualization of high-dimensional data, a new approach high dimensional data visualization Self-organizing map (HVSOM) is explained. The HVSOM preserve the inter-neuron distance and better visualizes the differences between the clusters. In HVSOM, the distances between input data points on the map resemble same those in the original space.
- Vydavateľstvo: LAP LAMBERT Academic Publishing
- Rok vydania: 2018
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9783659818172
