Combined modeling of sparse and dense noise improves Bayesian RVM

Combined modeling of sparse and dense noise improves Bayesian RVM. Sundin, M., Chatterjee, S., & Jansson, M. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 1841-1845, Sep., 2014.

Paper abstract bibtex

Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate sparse noise and sparse signal of interest. For estimating the sparse signal, without estimating the sparse noise, we construct a Relevance Vector Machine (RVM). In the RVM, sparse noise and ever present dense noise are treated through a combined noise model. Through simulations, we show the efficiency of new RVM for three applications: kernel regression, housing price prediction and compressed sensing.

@InProceedings{6952668,
  author = {M. Sundin and S. Chatterjee and M. Jansson},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Combined modeling of sparse and dense noise improves Bayesian RVM},
  year = {2014},
  pages = {1841-1845},
  abstract = {Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate sparse noise and sparse signal of interest. For estimating the sparse signal, without estimating the sparse noise, we construct a Relevance Vector Machine (RVM). In the RVM, sparse noise and ever present dense noise are treated through a combined noise model. Through simulations, we show the efficiency of new RVM for three applications: kernel regression, housing price prediction and compressed sensing.},
  keywords = {belief networks;regression analysis;signal processing;dense noise;Bayesian RVM approach;sparse noise models outliers;relevance vector machine;combined noise model;kernel regression;housing price prediction;compressed sensing;Noise;Bayes methods;Kernel;Vectors;Standards;Compressed sensing;Equations;Robust regression;Bayesian learning;Relevance vector machine;Compressed sensing},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922443.pdf},
}

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