9,915 research outputs found
Regression on fixed-rank positive semidefinite matrices: a Riemannian approach
The paper addresses the problem of learning a regression model parameterized
by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear
nature of the search space and on scalability to high-dimensional problems. The
mathematical developments rely on the theory of gradient descent algorithms
adapted to the Riemannian geometry that underlies the set of fixed-rank
positive semidefinite matrices. In contrast with previous contributions in the
literature, no restrictions are imposed on the range space of the learned
matrix. The resulting algorithms maintain a linear complexity in the problem
size and enjoy important invariance properties. We apply the proposed
algorithms to the problem of learning a distance function parameterized by a
positive semidefinite matrix. Good performance is observed on classical
benchmarks
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to
solve inverse problems in many computer vision applications. To learn such
models, the data samples are often clustered using the K-means algorithm with
the Euclidean distance as a dissimilarity metric. However, the Euclidean
distance may not always be a good dissimilarity measure for comparing data
samples lying on a manifold. In this paper, we propose two algorithms for
determining a local subset of training samples from which a good local model
can be computed for reconstructing a given input test sample, where we take
into account the underlying geometry of the data. The first algorithm, called
Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme
which can be seen as an out-of-sample extension of the replicator graph
clustering method for local model learning. The second method, called
Geometry-driven Overlapping Clusters (GOC), is a less complex nonadaptive
alternative for training subset selection. The proposed AGNN and GOC methods
are evaluated in image super-resolution, deblurring and denoising applications
and shown to outperform spectral clustering, soft clustering, and geodesic
distance based subset selection in most settings.Comment: 15 pages, 10 figures and 5 table
Equivariant semidefinite lifts of regular polygons
Given a polytope P in , we say that P has a positive
semidefinite lift (psd lift) of size d if one can express P as the linear
projection of an affine slice of the positive semidefinite cone
. If a polytope P has symmetry, we can consider equivariant psd
lifts, i.e. those psd lifts that respect the symmetry of P. One of the simplest
families of polytopes with interesting symmetries are regular polygons in the
plane, which have played an important role in the study of linear programming
lifts (or extended formulations). In this paper we study equivariant psd lifts
of regular polygons. We first show that the standard Lasserre/sum-of-squares
hierarchy for the regular N-gon requires exactly ceil(N/4) iterations and thus
yields an equivariant psd lift of size linear in N. In contrast we show that
one can construct an equivariant psd lift of the regular 2^n-gon of size 2n-1,
which is exponentially smaller than the psd lift of the sum-of-squares
hierarchy. Our construction relies on finding a sparse sum-of-squares
certificate for the facet-defining inequalities of the regular 2^n-gon, i.e.,
one that only uses a small (logarithmic) number of monomials. Since any
equivariant LP lift of the regular 2^n-gon must have size 2^n, this gives the
first example of a polytope with an exponential gap between sizes of
equivariant LP lifts and equivariant psd lifts. Finally we prove that our
construction is essentially optimal by showing that any equivariant psd lift of
the regular N-gon must have size at least logarithmic in N.Comment: 29 page
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