Asymptotic Escape of Spurious Critical Points on the Low-rank Matrix
Manifold
- URL: http://arxiv.org/abs/2107.09207v1
- Date: Tue, 20 Jul 2021 00:25:54 GMT
- Title: Asymptotic Escape of Spurious Critical Points on the Low-rank Matrix
Manifold
- Abstract summary: Given that the low-rank matrix manifold is an incomplete set, this result is the first to overcome this difficulty.
We show that using the dynamical low-rank approximation and a rescaled gradient flow, some of the spurious critical points can be converted to classical strict saddle points.
- Score: 2.692735698714241
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We show that the Riemannian gradient descent algorithm on the low-rank matrix
manifold almost surely escapes some spurious critical points on the boundary of
the manifold. Given that the low-rank matrix manifold is an incomplete set,
this result is the first to overcome this difficulty and partially justify the
global use of the Riemannian gradient descent on the manifold. The spurious
critical points are some rank-deficient matrices that capture only part of the
SVD components of the ground truth. They exhibit very singular behavior and
evade the classical analysis of strict saddle points. We show that using the
dynamical low-rank approximation and a rescaled gradient flow, some of the
spurious critical points can be converted to classical strict saddle points,
which leads to the desired result. Numerical experiments are provided to
support our theoretical findings.
Related papers
- Entrywise error bounds for low-rank approximations of kernel matrices [55.524284152242096]
We derive entrywise error bounds for low-rank approximations of kernel matrices obtained using the truncated eigen-decomposition.
A key technical innovation is a delocalisation result for the eigenvectors of the kernel matrix corresponding to small eigenvalues.
We validate our theory with an empirical study of a collection of synthetic and real-world datasets.
arXiv Detail & Related papers (2024-05-23T12:26:25Z) - Riemannian stochastic optimization methods avoid strict saddle points [68.80251170757647]
We show that policies under study avoid strict saddle points / submanifolds with probability 1.
This result provides an important sanity check as it shows that, almost always, the limit state of an algorithm can only be a local minimizer.
arXiv Detail & Related papers (2023-11-04T11:12:24Z) - Curvature-Independent Last-Iterate Convergence for Games on Riemannian
Manifolds [77.4346324549323]
We show that a step size agnostic to the curvature of the manifold achieves a curvature-independent and linear last-iterate convergence rate.
To the best of our knowledge, the possibility of curvature-independent rates and/or last-iterate convergence has not been considered before.
arXiv Detail & Related papers (2023-06-29T01:20:44Z) - The Inductive Bias of Flatness Regularization for Deep Matrix
Factorization [58.851514333119255]
This work takes the first step toward understanding the inductive bias of the minimum trace of the Hessian solutions in deep linear networks.
We show that for all depth greater than one, with the standard Isometry Property (RIP) on the measurements, minimizing the trace of Hessian is approximately equivalent to minimizing the Schatten 1-norm of the corresponding end-to-end matrix parameters.
arXiv Detail & Related papers (2023-06-22T23:14:57Z) - Critical Points and Convergence Analysis of Generative Deep Linear
Networks Trained with Bures-Wasserstein Loss [2.294014185517203]
We consider a deep matrix factorization model of covariance matrices trained with the Bures-Wasserstein distance.
We characterize the critical points and minimizers of the Bures-Wasserstein distance over the space of rank-bounded matrices.
We establish convergence results for gradient flow using a smooth perturbative version of the loss and convergence results for finite step size gradient descent.
arXiv Detail & Related papers (2023-03-06T10:56:14Z) - First-Order Algorithms for Min-Max Optimization in Geodesic Metric
Spaces [93.35384756718868]
min-max algorithms have been analyzed in the Euclidean setting.
We prove that the extraiteient (RCEG) method corrected lastrate convergence at a linear rate.
arXiv Detail & Related papers (2022-06-04T18:53:44Z) - Convergence of gradient descent for learning linear neural networks [2.209921757303168]
We show that gradient descent converges to a critical point of the loss function, i.e., the square loss in this article.
In the case of three or more layers we show that gradient descent converges to a global minimum on the manifold matrices of some fixed rank.
arXiv Detail & Related papers (2021-08-04T13:10:30Z) - Small random initialization is akin to spectral learning: Optimization
and generalization guarantees for overparameterized low-rank matrix
reconstruction [35.585697639325105]
In this paper we show that small random initialization are not fully understood.
We reconstruct a gradient from a small randomrank matrix and find solutions akin to an optimal gradient from a low randomrank matrix.
arXiv Detail & Related papers (2021-06-28T22:52:39Z) - High-probability Bounds for Non-Convex Stochastic Optimization with
Heavy Tails [55.561406656549686]
We consider non- Hilbert optimization using first-order algorithms for which the gradient estimates may have tails.
We show that a combination of gradient, momentum, and normalized gradient descent convergence to critical points in high-probability with best-known iteration for smooth losses.
arXiv Detail & Related papers (2021-06-28T00:17:01Z) - Spectral clustering under degree heterogeneity: a case for the random
walk Laplacian [83.79286663107845]
This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree.
In the special case of a degree-corrected block model, the embedding concentrates about K distinct points, representing communities.
arXiv Detail & Related papers (2021-05-03T16:36:27Z) - Boundary Conditions for Linear Exit Time Gradient Trajectories Around
Saddle Points: Analysis and Algorithm [9.69596041242667]
An understanding of multiple objective functions in a landscape of strict-saddle points is presented.
An analysis of the neighborhoods convergence to a local landscape that has a maximum of strict-saddle points is also presented.
arXiv Detail & Related papers (2021-01-07T16:59:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.