Changes between Version 18 and Version 19 of MatrixFactorization


Ignore:
Timestamp:
07/21/11 15:15:01 (3 years ago)
Author:
MarinkaZitnik
Comment:

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  • MatrixFactorization

    v18 v19  
    2222 
    2323=== ns-nmf === 
    24 Non-smooth NMF. Uses a modifed version of Lee and Seung's multiplicative updates for Kullbach-Leibler divergence. It is meant to give sparser results.[[BR]] 
     24Non-smooth NMF. Uses a modified version of Lee and Seung's multiplicative updates for Kullbach-Leibler divergence. It is meant to give sparser results.[[BR]] 
    2525Reference: (Pascual-Montano, 2006). 
    2626 
     
    3434 
    3535=== pmf === 
    36 Probabilistic MF. PMF model scales linearly with the number of observations and performs well on large, sparse, imbalanced datasets.[[BR]] 
    37 Reference: (Salakhutdinov, Mnih 2008 (NIPS)). 
    38  
    39 === Additional: bpmf === 
    40 Bayesian PMF model that implements Gibbs sampler. In Bayesian PMF model capacity is controlled automatically by integrating over all model parameters and hyperparameters. Bayesian PMF can be efficiently trained using MCMC methods. This model achieves significantly higher prediction accuracy than PMF models trained using MAP estimation. This algorithm has been used by winning and top ranked teams in KDD Cup 2011. [[BR]] 
    41 Reference: (Salakhutdinov, Mnih, 2008 (ICML)). 
     36Probabilistic MF. Target matrix is interpreted as samples from multinomial. [[BR]] 
     37Reference: (Hansen, 2008), (Laurberg, 2008). 
    4238 
    4339=== psmf === 
     
    145141 * Zhang, Z., Li, T., Ding, C. H. Q., Zhang, X. Binary Matrix Factorization with Applications. ICDM 2007. 
    146142 * Salakhutdinov, R., Mnih, A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. ICML 2008, 880-887.  
     143 * Laurberg, H.,et. al. Theorems on positive data: on the uniqueness of NMF. Computational intelligence and neuroscience. 2008. 
     144 * Hansen, L. K. Generalization in high-dimensional factor models. 2008. Web: http://www.stanford.edu/group/mmds/slides2008/hansen.pdf.