Signal Classification using Smooth Coefficients of Multiple wavelets
Classification of time series signals has become an important construct and has many practical applications. With existing classifiers we may be able to accurately classify signals, however that accuracy may decline if using a reduced number of attributes. Transforming the data then undertaking reduction in dimensionality may improve the quality of the data analysis, decrease time required for classification and simplify models. We propose an approach, which chooses suitable wavelets to transform the data, then combines the output from these transforms to construct a dataset to then apply ensemble classifiers to. We demonstrate this on different data sets, across different classifiers and use differing evaluation methods. Our experimental results demonstrate the effectiveness of the proposed technique, compared to the approaches that use either raw signal data or a single wavelet transform.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification
This paper introduces a Deep Scattering network that utilizes Dual-Tree complex wavelets to extract translation invariant representations from an input signal. The computationally efficient Dual-Tree wavelets decompose t…
ClassificationGeneral ClassificationTranslationWaveletStereo: Learning Wavelet Coefficients of Disparity Map in Stereo Matching
Some stereo matching algorithms based on deep learning have been proposed and achieved state-of-the-art performances since some public large-scale datasets were put online. However, the disparity in smooth regions and de…
Stereo MatchingMultiresolution local smoothness detection in non-uniformly sampled multivariate signals
Inspired by edge detection based on the decay behavior of wavelet coefficients, we introduce a (near) linear-time algorithm for detecting the local regularity in non-uniformly sampled multivariate signals. Our approach q…
Image SegmentationEdge DetectionPoint CloudsClassification by sparse generalized additive models
We consider (nonparametric) sparse (generalized) additive models (SpAM) for classification. The design of a SpAM classifier is based on minimizing the logistic loss with a sparse group Lasso/Slope-type penalties on the c…
Additive modelsClassificationScattering Networks on Noncommutative Finite Groups
Scattering Networks were initially designed to elucidate the behavior of early layers in Convolutional Neural Networks (CNNs) over Euclidean spaces and are grounded in wavelets. In this work, we introduce a scattering tr…