Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation
This work addresses clustering challenges in noisy time-series data for applications like signal processing, but it appears incremental as it builds on existing sparse K-means and scattering transform methods.
The authors tackled the problem of clustering noisy time-series signals with low signal-to-noise ratio by developing a framework that combines structured sparsity, wavelets, and scattering transforms for simultaneous smoothing and dimensionality reduction. The result was improved clustering performance on several real datasets, though no specific numerical gains were provided.
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.