Thursday, October 22, 2015

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:

 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: 
Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments: