Mikkel N. Schmidt

Publications (4)1.88 Total impact

• Article: Bayesian community detection.

  Morten Mørup, Mikkel N Schmidt
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  ABSTRACT: Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled.
  Neural Computation 04/2012; 24(9):2434-56. · 1.88 Impact Factor
• Source

  Available from: ArXiv

  Article: Infinite Multiple Membership Relational Modeling for Complex Networks

  Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen
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  ABSTRACT: Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiple-membership models that scale quadratically in the number of vertices the proposed model scales linearly in the number of links admitting multiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show on "real" size benchmark network data that accounting for multiple memberships improves the learning of latent structure as measured by link prediction while explicitly accounting for multiple membership result in a more compact representation of the latent structure of networks.
  01/2011;
• Source

  Available from: Ole Winther

  Conference Proceeding: Bayesian Non-negative Matrix Factorization.

  Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen
  Independent Component Analysis and Signal Separation, 8th International Conference, ICA 2009, Paraty, Brazil, March 15-18, 2009. Proceedings; 01/2009
• Source

  Available from: eprints.pascal-network.org

  Chapter: Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation

  Mikkel N. Schmidt, Morten Mørup
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  ABSTRACT: We present a novel method for blind separation of instruments in single channel polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. The method is an extention of NMFD recently introduced by Smaragdis [1]. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to individual instruments. Based on this factorization we separate the instruments using spectrogram masking. The proposed algorithm has applications in computational auditory scene analysis, music information retrieval, and automatic music transcription.
  02/2006: pages 700-707;