Context Machines: A Series of Situated and Self-Organizing Artworks

Leonardo ◽  
2013 ◽  
Vol 46 (2) ◽  
pp. 114-122 ◽  
Author(s):  
Benjamin David Robert Bogart ◽  
Philippe Pasquier
Keyword(s):  
High Resolution ◽  
The Self ◽  
Self Organizing Map ◽  
Site Specific ◽  
Photographic Images ◽  
Associative Process ◽  
Self Organized ◽  
Over Time ◽  
Self Organizing

The authors discuss the development of self-organizing artworks. Context Machines are a family of site-specific, conceptual and generative artworks that capture photographic images from their environment in the construction of creative compositions. Resurfacing produces interactive temporal landscapes from images captured over time. Memory Association Machine's free-associative process, modeled after Gabora's theory of creativity, traverses a self-organized map of images collected from the environment. In the Dreaming Machine installations, these free associations are framed as dreams. The self-organizing map is applied to thousands of images in Self-Organized Landscapes—high-resolution collages intended for print reproduction. Context Machines invite us to reconsider what is essentially human and to look at ourselves, and our world, anew.

scholarly journals An EEG Feature Extraction Method Based on Sparse Dictionary Self-Organizing Map for Event-Related Potential Recognition

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 259
Author(s):  
Shang Feng ◽  
Haifeng Li ◽  
Lin Ma ◽  
Zhongliang Xu
Keyword(s):  
Feature Extraction ◽  
Event Related Potential ◽  
The Self ◽  
Computer Interface ◽  
Self Organizing Map ◽  
Eeg Signal ◽  
Sparse Decomposition ◽  
Feature Extraction Method ◽  
Self Organized ◽  
Self Organizing

In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical meaning, and cannot relate to actual EEG waveforms, especially event-related potential waveforms. In this article, we propose a feature extraction method based on a self-organizing map of sparse dictionary atoms, which can aggregate event-related potential waveforms scattered inside an over-complete sparse dictionary into the code book of neurons in the self-organizing map network. Then, the cosine similarity between the EEG signal sample and the code vector is used as the classification feature. Compared with traditional feature extraction methods based on sparse decomposition, the classification features obtained by this method have more intuitive electrophysiological meaning. The experiment conducted on a public auditory event-related potential (ERP) brain-computer interface dataset showed that, after the self-organized mapping of dictionary atoms, the neurons’ code vectors in the self-organized mapping network were remarkably similar to the ERP waveform obtained after superposition and averaging. The feature extracted by the proposed method used a smaller amount of data to obtain classification accuracy comparable to the traditional method.

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

VISUAL APPROACH TO SUPERVISED VARIABLE SELECTION BY SELF-ORGANIZING MAP

2005 ◽  
Vol 15 (01n02) ◽  
pp. 101-110 ◽  
Author(s):  
TIMO SIMILÄ ◽  
SAMPSA LAINE
Keyword(s):  
Variable Selection ◽  
The Self ◽  
Data Sets ◽  
Self Organizing Map ◽  
Robust Method ◽  
Relevant Variables ◽  
Visual Approach ◽  
Predefined Criterion ◽  
Target Data ◽  
Self Organizing

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task. The user manually selects a set of target variables and trains a Self-Organizing Map with these data. This sets a criterion to variable selection and is an illustrative description of the user's problem, even for multivariate target data. The user also defines another set of variables that are potentially related to the problem. Our method returns a subset of these variables, which best corresponds to the description provided by the Self-Organizing Map and, thus, agrees with the user's understanding about the problem. The method is conceptually simple and, based on experiments, allows an accessible approach to supervised variable selection.

Representation and Identification of Fault Conditions of an Anaesthesia System by Means of the Self-Organizing Map

ICANN ’94 ◽  
1994 ◽  
pp. 350-353 ◽  
Author(s):  
Mauri Vapola ◽  
Olli Simula ◽  
Teuvo Kohonen ◽  
Pekka Meriläinen
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Robust classification with reject option using the self-organizing map

2015 ◽  
Vol 26 (7) ◽  
pp. 1603-1619 ◽  
Author(s):  
Ricardo Gamelas Sousa ◽  
Ajalmar R. Rocha Neto ◽  
Jaime S. Cardoso ◽  
Guilherme A. Barreto
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Reject Option ◽  
Robust Classification ◽  
Self Organizing
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