32 research outputs found
Mutual Information in Rank-One Matrix Estimation
We consider the estimation of a n-dimensional vector x from the knowledge of
noisy and possibility non-linear element-wise measurements of xxT , a very
generic problem that contains, e.g. stochastic 2-block model, submatrix
localization or the spike perturbation of random matrices. We use an
interpolation method proposed by Guerra and later refined by Korada and Macris.
We prove that the Bethe mutual information (related to the Bethe free energy
and conjectured to be exact by Lesieur et al. on the basis of the non-rigorous
cavity method) always yields an upper bound to the exact mutual information. We
also provide a lower bound using a similar technique. For concreteness, we
illustrate our findings on the sparse PCA problem, and observe that (a) our
bounds match for a large region of parameters and (b) that it exists a phase
transition in a region where the spectum remains uninformative. While we
present only the case of rank-one symmetric matrix estimation, our proof
technique is readily extendable to low-rank symmetric matrix or low-rank
symmetric tensor estimationComment: 8 pages, 1 figure
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
Factorizing low-rank matrices has many applications in machine learning and
statistics. For probabilistic models in the Bayes optimal setting, a general
expression for the mutual information has been proposed using heuristic
statistical physics computations, and proven in few specific cases. Here, we
show how to rigorously prove the conjectured formula for the symmetric rank-one
case. This allows to express the minimal mean-square-error and to characterize
the detectability phase transitions in a large set of estimation problems
ranging from community detection to sparse PCA. We also show that for a large
set of parameters, an iterative algorithm called approximate message-passing is
Bayes optimal. There exists, however, a gap between what currently known
polynomial algorithms can do and what is expected information theoretically.
Additionally, the proof technique has an interest of its own and exploits three
essential ingredients: the interpolation method introduced in statistical
physics by Guerra, the analysis of the approximate message-passing algorithm
and the theory of spatial coupling and threshold saturation in coding. Our
approach is generic and applicable to other open problems in statistical
estimation where heuristic statistical physics predictions are available
Information-theoretic bounds and phase transitions in clustering, sparse PCA, and submatrix localization
We study the problem of detecting a structured, low-rank signal matrix
corrupted with additive Gaussian noise. This includes clustering in a Gaussian
mixture model, sparse PCA, and submatrix localization. Each of these problems
is conjectured to exhibit a sharp information-theoretic threshold, below which
the signal is too weak for any algorithm to detect. We derive upper and lower
bounds on these thresholds by applying the first and second moment methods to
the likelihood ratio between these "planted models" and null models where the
signal matrix is zero. Our bounds differ by at most a factor of root two when
the rank is large (in the clustering and submatrix localization problems, when
the number of clusters or blocks is large) or the signal matrix is very sparse.
Moreover, our upper bounds show that for each of these problems there is a
significant regime where reliable detection is information- theoretically
possible but where known algorithms such as PCA fail completely, since the
spectrum of the observed matrix is uninformative. This regime is analogous to
the conjectured 'hard but detectable' regime for community detection in sparse
graphs.Comment: For sparse PCA and submatrix localization, we determine the
information-theoretic threshold exactly in the limit where the number of
blocks is large or the signal matrix is very sparse based on a conditional
second moment method, closing the factor of root two gap in the first versio