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Moreover, recent approaches allow an EEG data representation with adaptive and sparse decompositions [13].
They even introduced the contexts of data representation with different dynamics.
Plenoptic video, also known as light field video, is an emerging visual data representation with known applications in computational photography, microscopy, visual inspection and medical imaging among others.
Therefore we present some aspects relating to feature based design, standardized product data representation with ISO 10303 and neutral data exchange with STEP (Standard for The Exchange of Product model data).
The alternative, using conventional data representations where the data models cannot evolve, typically requires the biomedical community to complement the data representation with a clandestine and inefficient flurry of datasets exchanged as spreadsheets through email.
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Deep learning is to apply computational models to learn data representations with multiple levels of abstraction [1].
More recently, a particular method, namely Random Projection, has been shown to have the advantage of high quality data representations with minimal computation effort, even for data dimensions in the range of hundreds of thousands or even millions.
This indicated the adequacy of the data representation model with no confounding with environmental trends and the accuracy of the genetic parameter estimates in the base population.
By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively.
This shows that the stringency of the data representation provided with SVD is sufficient to infer Linnaean taxonomy levels.
The development of a common framework for data representation provides users with a large amount of flexibility to develop new tools for managing the data while maintaining interoperability with external resources.
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