Soft-LOST: EM on a mixture of oriented lines. Robust clustering of data into overlapping linear subspaces is a common problem. Here we consider one-dimensional subspaces that cross the origin. This problem arises in blind source separation, where the subspaces correspond directly to columns of a mixing matrix. We present an algorithm that identifies these subspaces using an EM procedure, where the E-step calculates posterior probabilities assigning data points to lines and M-step repositions the lines to match the points assigned to them. This method, combined with a transformation into a sparse domain and an L 1-norm optimisation, constitutes a blind source separation algorithm for the under-determined case.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Kühne, Marco; Togneri, Roberto; Nordholm, Sven: A novel fuzzy clustering algorithm using observation weighting and context information for reverberant blind speech separation (2010)
- He, Zhaoshui; Cichocki, Andrzej; Li, Yuanqing; Xie, Shengli; Sanei, Saeid: K-hyperline clustering learning for sparse component analysis (2009)
- O’Grady, Paul D.; Pearlmutter, Barak A.: The LOST algorithm: Finding lines and separating speech mixtures (2008)
- Melia, Thomas; Rickard, Scott: Underdetermined blind source separation in echoic environments using DESPRIT (2007)
- Mitianoudis, Nikolaos; Stathaki, Tania: Underdetermined source separation using mixtures of warped Laplacians (2007)
- Vincent, Emmanuel; Sawada, Hiroshi; Bofill, Pau; Makino, Shoji; Rosca, Justinian P.: First stereo audio source separation evaluation campaign: Data, algorithms and results (2007)
- He, Zhaoshui; Cichocki, Andrzej: K-EVD clustering and its applications to sparse component analysis (2006)