Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation - implementation -

Or just let me know of his recent arxiv preprint:

Hi Igor,


Hope all is well,

The following manuscript on sparse clustering using time-frequency representation by students Tom Hope, Avishai Wagner and me might be of interest to your blog's reader:
http://arxiv.org/abs/1510.05214

Best,
Or

 Thanks Or ! Here it is:Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation by Tom Hope, Avishai Wagner, Or Zuk

We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information. We extend the sparse K-means algorithm by incorporating structured sparsity, and use it to exploit the multi-scale property of wavelets and group structure in multivariate signals. Finally, we extract features invariant to translation and scaling with the scattering transform, which corresponds to a convolutional network with filters given by a wavelet operator, and use the network's structure in sparse clustering. By promoting sparsity, this transform can yield a low-dimensional representation of signals that gives improved clustering results on several real datasets.

SPARCWave is on Github: https://github.com/avishaiwa/SPARCWave 

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