Understanding Wavelets, Part 5: Machine Learning and Deep Learning with Wavelet Scattering

Wavelet scattering networks help you obtain low-variance features from signals and images for use in machine learning and deep learning applications. Scattering networks help you automatically obtain features that minimize differences within a class while preserving discriminability across classes. An important distinction between the scattering network and deep learning framework is that the filters are defined a priori as opposed to being learned as in the case of deep convolutional networks. As the scattering transform is not required to learn the filters, you can often use scattering successfully in situations where there is shortage of training data. You can also visualize and interpret the features extracted by the wavelet scattering network. Once the features are extracted, you can train and evaluate various machine learning algorithms, such as support vector machine (SVM) and random forest, or deep learning algorithms, such as long short-term memory (LSTM) networks.

--------------------------------------------------------------------------------------------------------
Get a free product Trial: https://goo.gl/ZHFb5u
Learn more about MATLAB: https://goo.gl/8QV7ZZ
Learn more about Simulink: https://goo.gl/nqnbLe
See What's new in MATLAB and Simulink: https://goo.gl/pgGtod

© 2019 The MathWorks, Inc. MATLAB and Simulink are registered
trademarks of The MathWorks, Inc.
See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.