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The main drawback is that most formats require a data compression method that tosses out some audio information to make files smaller.
Therefore, a data compression method has to be used for the sake of reducing the volume of data.
Therefore, a data compression method is necessary to store and process this information while preserving as much information as possible.
A data compression method based on LOT (Lapped Orthogonal Transform) with adaptive thresholding of the transform coefficients has also been proposed.
Vector quantization (VQ) is a data compression method.
LZW is a data compression method that takes advantage of this repetition.
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PPM is a statistical data compression method based on N-order Markov models.
Recently, Indahl et. al. [ 41] proposed a new data compression method for estimating optimal latent variables by combining PLS methodology and canonical correlation analysis (CCA).
Among recent advancements in PLS methodology itself we find that Indahl et. al. [ 41] propose a new data compression method for estimating optimal latent variables classification and regression problems by combining PLS methodology and canonical correlation analysis (CCA), called Canonical Powered PLS CPPLSS).
In this work, we present a new test data compression method based on a stored set called Lingering Component Reduction (LCR) Code.
This shows that if the gradients are required in the data analysis stage, CS should not be used as the compression method whereas direct WC is still a viable lossy data compression method.
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Justyna Jupowicz-Kozak
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