18,635 research outputs found
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM
Permutation testing is a non-parametric method for obtaining the max null
distribution used to compute corrected -values that provide strong control
of false positives. In neuroimaging, however, the computational burden of
running such an algorithm can be significant. We find that by viewing the
permutation testing procedure as the construction of a very large permutation
testing matrix, , one can exploit structural properties derived from the
data and the test statistics to reduce the runtime under certain conditions. In
particular, we see that is low-rank plus a low-variance residual. This
makes a good candidate for low-rank matrix completion, where only a very
small number of entries of ( of all entries in our experiments)
have to be computed to obtain a good estimate. Based on this observation, we
present RapidPT, an algorithm that efficiently recovers the max null
distribution commonly obtained through regular permutation testing in
voxel-wise analysis. We present an extensive validation on a synthetic dataset
and four varying sized datasets against two baselines: Statistical
NonParametric Mapping (SnPM13) and a standard permutation testing
implementation (referred as NaivePT). We find that RapidPT achieves its best
runtime performance on medium sized datasets (), with
speedups of 1.5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT). For larger
datasets () RapidPT outperforms NaivePT (6x - 200x) on all
datasets, and provides large speedups over SnPM13 when more than 10000
permutations (2x - 15x) are needed. The implementation is a standalone toolbox
and also integrated within SnPM13, able to leverage multi-core architectures
when available.Comment: 36 pages, 16 figure
Speeding up Permutation Testing in Neuroimaging
Multiple hypothesis testing is a significant problem in nearly all
neuroimaging studies. In order to correct for this phenomena, we require a
reliable estimate of the Family-Wise Error Rate (FWER). The well known
Bonferroni correction method, while simple to implement, is quite conservative,
and can substantially under-power a study because it ignores dependencies
between test statistics. Permutation testing, on the other hand, is an exact,
non-parametric method of estimating the FWER for a given -threshold,
but for acceptably low thresholds the computational burden can be prohibitive.
In this paper, we show that permutation testing in fact amounts to populating
the columns of a very large matrix . By analyzing the spectrum of this
matrix, under certain conditions, we see that has a low-rank plus a
low-variance residual decomposition which makes it suitable for highly
sub--sampled --- on the order of --- matrix completion methods. Based
on this observation, we propose a novel permutation testing methodology which
offers a large speedup, without sacrificing the fidelity of the estimated FWER.
Our evaluations on four different neuroimaging datasets show that a
computational speedup factor of roughly can be achieved while
recovering the FWER distribution up to very high accuracy. Further, we show
that the estimated -threshold is also recovered faithfully, and is
stable.Comment: NIPS 1
Dense Error Correction for Low-Rank Matrices via Principal Component Pursuit
We consider the problem of recovering a low-rank matrix when some of its
entries, whose locations are not known a priori, are corrupted by errors of
arbitrarily large magnitude. It has recently been shown that this problem can
be solved efficiently and effectively by a convex program named Principal
Component Pursuit (PCP), provided that the fraction of corrupted entries and
the rank of the matrix are both sufficiently small. In this paper, we extend
that result to show that the same convex program, with a slightly improved
weighting parameter, exactly recovers the low-rank matrix even if "almost all"
of its entries are arbitrarily corrupted, provided the signs of the errors are
random. We corroborate our result with simulations on randomly generated
matrices and errors.Comment: Submitted to ISIT 201
D3-instantons, Mock Theta Series and Twistors
The D-instanton corrected hypermultiplet moduli space of type II string
theory compactified on a Calabi-Yau threefold is known in the type IIA picture
to be determined in terms of the generalized Donaldson-Thomas invariants,
through a twistorial construction. At the same time, in the mirror type IIB
picture, and in the limit where only D3-D1-D(-1)-instanton corrections are
retained, it should carry an isometric action of the S-duality group SL(2,Z).
We prove that this is the case in the one-instanton approximation, by
constructing a holomorphic action of SL(2,Z) on the linearized twistor space.
Using the modular invariance of the D4-D2-D0 black hole partition function, we
show that the standard Darboux coordinates in twistor space have modular
anomalies controlled by period integrals of a Siegel-Narain theta series, which
can be canceled by a contact transformation generated by a holomorphic mock
theta series.Comment: 42 pages; discussion of isometries is amended; misprints correcte
Involution and Constrained Dynamics I: The Dirac Approach
We study the theory of systems with constraints from the point of view of the
formal theory of partial differential equations. For finite-dimensional systems
we show that the Dirac algorithm completes the equations of motion to an
involutive system. We discuss the implications of this identification for field
theories and argue that the involution analysis is more general and flexible
than the Dirac approach. We also derive intrinsic expressions for the number of
degrees of freedom.Comment: 28 pages, latex, no figure
Rank Minimization over Finite Fields: Fundamental Limits and Coding-Theoretic Interpretations
This paper establishes information-theoretic limits in estimating a finite
field low-rank matrix given random linear measurements of it. These linear
measurements are obtained by taking inner products of the low-rank matrix with
random sensing matrices. Necessary and sufficient conditions on the number of
measurements required are provided. It is shown that these conditions are sharp
and the minimum-rank decoder is asymptotically optimal. The reliability
function of this decoder is also derived by appealing to de Caen's lower bound
on the probability of a union. The sufficient condition also holds when the
sensing matrices are sparse - a scenario that may be amenable to efficient
decoding. More precisely, it is shown that if the n\times n-sensing matrices
contain, on average, \Omega(nlog n) entries, the number of measurements
required is the same as that when the sensing matrices are dense and contain
entries drawn uniformly at random from the field. Analogies are drawn between
the above results and rank-metric codes in the coding theory literature. In
fact, we are also strongly motivated by understanding when minimum rank
distance decoding of random rank-metric codes succeeds. To this end, we derive
distance properties of equiprobable and sparse rank-metric codes. These
distance properties provide a precise geometric interpretation of the fact that
the sparse ensemble requires as few measurements as the dense one. Finally, we
provide a non-exhaustive procedure to search for the unknown low-rank matrix.Comment: Accepted to the IEEE Transactions on Information Theory; Presented at
IEEE International Symposium on Information Theory (ISIT) 201
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