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The algorithm uses the DIF method for decomposing data.
Certain matrices required by the predictor are calculated by decomposing data into past and future sections as used in the subspace identification method.
Non-negative matrix factorization (NMF) [9, 10, 19] is another approach which has been proven a useful tool for decomposing data into part-based components.
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GIFT software automatically decomposed data into 39 components.
Then, the decomposed data were used for further modeling processes.
It decomposes data based on the rules dedicated for tag storage optimization and redundancy.
It has an ability to extract useful information from multiple non-stationary signals simultaneously by analysing in the decomposed data through principal component analysis (PCA).
However, our contribution is to employ the DIF method to decompose data, i.e., to prepare it for convolution so that it can be processed in parts.
Tensor decomposed data were fed into the inversion code with a 10% error floor for the apparent resistivity and 2.86° for the phase.
For the out-of-place decomposition of the data, 2αKLM bytes are required for storing the signal and the kernel and 4αKLM bytes for the decomposed data since complex numbers use up twice the size of real numbers.
In this case, error-correction serves as a synchrony finder with free parameter k, similar to how "K-means" is a standard unsupervised approach to decompose data into K clusters.
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