Matrix Dimensionality Compression Algorithm

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A Matrix Dimensionality Compression Algorithm is a dataset dimensionality compression algorithm/matrix compression algorithm that can be applied by a matrix dimensionality compression system (to solve a matrix dimensionality compression task).




    • In pattern recognition and in image processing, Feature extraction is a special form of dimensionality reduction.
    • When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information) then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called features extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input.