1,879 research outputs found
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models
We propose maximum likelihood estimation for learning Gaussian graphical
models with a Gaussian (ell_2^2) prior on the parameters. This is in contrast
to the commonly used Laplace (ell_1) prior for encouraging sparseness. We show
that our optimization problem leads to a Riccati matrix equation, which has a
closed form solution. We propose an efficient algorithm that performs a
singular value decomposition of the training data. Our algorithm is
O(NT^2)-time and O(NT)-space for N variables and T samples. Our method is
tailored to high-dimensional problems (N gg T), in which sparseness promoting
methods become intractable. Furthermore, instead of obtaining a single solution
for a specific regularization parameter, our algorithm finds the whole solution
path. We show that the method has logarithmic sample complexity under the
spiked covariance model. We also propose sparsification of the dense solution
with provable performance guarantees. We provide techniques for using our
learnt models, such as removing unimportant variables, computing likelihoods
and conditional distributions. Finally, we show promising results in several
gene expressions datasets.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
From random walks to distances on unweighted graphs
Large unweighted directed graphs are commonly used to capture relations
between entities. A fundamental problem in the analysis of such networks is to
properly define the similarity or dissimilarity between any two vertices.
Despite the significance of this problem, statistical characterization of the
proposed metrics has been limited. We introduce and develop a class of
techniques for analyzing random walks on graphs using stochastic calculus.
Using these techniques we generalize results on the degeneracy of hitting times
and analyze a metric based on the Laplace transformed hitting time (LTHT). The
metric serves as a natural, provably well-behaved alternative to the expected
hitting time. We establish a general correspondence between hitting times of
the Brownian motion and analogous hitting times on the graph. We show that the
LTHT is consistent with respect to the underlying metric of a geometric graph,
preserves clustering tendency, and remains robust against random addition of
non-geometric edges. Tests on simulated and real-world data show that the LTHT
matches theoretical predictions and outperforms alternatives.Comment: To appear in NIPS 201
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