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SNA – Machine Learning – Dimensionality reduction

Visualization of a social network analysis (SNA) defined by a high-dimensional parameter space (21 dimensions). Dimensionality reduction (DR) is used here to allow the reduction of the number of variables that defines a system, into a lower-dimensional space (2D). In this case, I’m using self-organizing maps (SOM), which represent a type of unsupervised artificial neural network, to reduce the high dimensionality of the data whilst also retain the high-dimensional non-linear associations.
Another feature extraction used (side by side image), is a linear feature extraction method -> K-means cluster analysis, (although linear methods don’t retain the associations distributed non-linearly in the high dimensional space). #owl #machinelearning
  As the dataset was relatively small, I used only CPU ( 2 x Intel Core i7-4930k @ 3.40GHz) computing for the training phase. If you are dealing with a big data set, a GPU  might be a better way to go to train your dataset. CPU – 2 x Intel Core i7-4930k @ 3.40GHz GPU – GPU
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nonstandardstudio - deep learning, creative ai, generative algorithms, genetic algorithms, parametric design, advanced computational design, architectureDEEP LEARNING Research augmented creativity design space explorer 3d domain translations Computer vision generative adversarial networks neural networks MACHINE LEARNING dimensionality reduction ADVANCED Computational Design genetic algorithms generative algorithms AGENT BASED SYSTEMS multi-agent systems structural encoded agents
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nonstandardstudio - deep learning, creative ai, generative algorithms, genetic algorithms, parametric design, advanced computational design, architecture
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