Vector quantization and signal compression
Vector quantization and signal compression
Ten lectures on wavelets
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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A problem of learning from a database where each sample consists of several time series and a single response is considered. We are interested in maximum data reduction that preserves predictive power of the original time series, and at the same time allows reasonable reconstruction quality of the original signals. Each signal is decomposed into a set of wavelet features that are coded according to their importance consisting of two terms. The first depends on the influence of the feature on the expected signal reconstruction error, and the second is determined by feature importance for the response prediction. The latter is calculated by building series of boosted decision tree ensembles. We demonstrate that such combination maintains small signal distortion rates, and ensures no increase in the prediction error in contrast to the unsupervised compression with the same reduction ratio.