Overcomplete Independent Component Analysis via SDP

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Podosinnikova, Anastasia; Perry, Amelia; Wein, Alexander; Bach, Francis; d'Aspremont, Alexandre; Sontag, David;
(2019)
  • Subject: Statistics - Machine Learning | Computer Science - Machine Learning

We present a novel algorithm for overcomplete independent components analysis (ICA), where the number of latent sources k exceeds the dimension p of observed variables. Previous algorithms either suffer from high computational complexity or make strong assumptions about... View more
  • References (44)
    44 references, page 1 of 5

    A. Anandkumar, R. Ge, and M. Janzamin. Learning overcomplete latent variable models through tensor methods. In Proceedings of the Conference on Learning Theory (COLT), 2015.

    S. Arora, R. Ge, A. Moitra, and S. Sachdeva. Provable ICA with unknown Gaussian noise, with implications for Gaussian mixtures and autoencoders. In Advances in Neural Information Processing Systems (NIPS), 2012.

    A. Beck and M. Teboulle. A fast iterative shrinkagethresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009.

    Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798-1828, 2013.

    A. Bhardwaj, P. Rostalski, and R. Sanyal. Deciding polyhedrality of spectrahedra. SIAM Journal on Optimization, 25(3):1873-1884, 2015.

    A. Bhaskara, M. Charikar, A. Moitra, and A. Vijayaraghavan. Smoothed analysis of tensor decompositions. In Proceedings of the Annual ACM Symposium on Theory of Computing (STOC), 2014a.

    A. Bhaskara, M. Charikar, and A. Vijayaraghavan. Uniqueness of tensor decompositions with applications to polynomial idenfifiability. In Proceedings of the Conference on Learning Theorey (COLT), 2014b.

    A. Bovier. Extreme Values of Random Processes. Lecture Notes Technische Universita¨t Berlin, 2005.

    A. Bunse-Gerstner, R. Byers, and V. Mehrmann. Numerical methods for simultaneous diagonalization. SIAM Journal on Matrix Analysis and Applications, 14(4):927-949, 1993.

    J.-F. Cardoso and A. Souloumiac. Blind beamforming for non-Gaussian signals. In IEE Proceedings F - Radar and Signal Processing. IEEE, 1993.

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