A sequence ${T_n}_{n=1}^{infty}$ of bounded linear  operators on a separable infinite dimensional Hilbert space $mathcal{H}$ is called subspace-diskcyclic with respect to the closed subspace $Msubseteq mathcal{H},$ if there exists a vector $xin mathcal{H}$ such that the disk-scaled orbit ${alpha T_n x: nin mathbb{N}, alpha inmathbb{C}, | alpha | leq 1}cap M$ is dense in $M$. The goal of t...

This is a survey over recent asymptotic results on random polytopes in d-dimensional Euclidean space. Three ways of generating a random polytope are considered: convex hulls of finitely many random points, projections of a fixed highdimensional polytope into a random d-dimensional subspace, intersections of random closed halfspaces. The type of problems for which asymptotic results are describe...

Subspace learning (SL) plays an important role in hyperspectral image (HSI) classification, since it can provide effective solution to reduce the redundant information pixels of HSIs. Previous works about SL aim improve accuracy HSI recognition. Using a large number labeled samples, related methods train parameters proposed solutions obtain better representations pixels. However, data instances...

Sinusoidal signals with random time-varying amplitude show up in many signal processing applications. Amplitude modulation results in degeneracy of the signal subspace, i.e. the signal subspace corresponding to one amplitude modulated sinusoid is no longer spanned by one vector. In this paper, we propose modi cations of two subspace-based techniques, namely ESPRIT and MODE for estimating the ce...

Construction of subspace codes with good parameters is one of the most important problems in random network coding. In this paper we present first a generalization of the concept of cyclic subspaces codes and further we show that the usual methods for constructing cyclic subspace codes over finite fields works for m-quasi cyclic codes, namely the subspaces polynomials and Frobenius mappings.

In this paper, we represent an inexact inverse subspace iteration method for computing a few eigenpairs of the generalized eigenvalue problem Ax = Bx [Q. Ye and P. Zhang, Inexact inverse subspace iteration for generalized eigenvalue problems, Linear Algebra and its Application, 434 (2011) 1697-1715 ]. In particular, the linear convergence property of the inverse subspace iteration is preserved.

Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace. In many practical scenarios, the dimensionality of data points to be clustered are compressed due to constraints of measurement, computation or privacy. In this paper, we study the theoretical properties...

Over the past several decades, subspace clustering has been receiving increasing interest and continuous progress. However, due to the lack of scalability and/or robustness, existing methods still have difficulty in dealing with the data that possesses simultaneously three characteristics: high-dimensional, massive and grossly corrupted. To tackle the scalability and robustness issues simultane...

Abstract—Codes constructed as subsets of the projective geometry of a vector space over a finite field have recently been shown to have applications as random network error correcting codes. If the dimension of each codeword is restricted to a fixed integer, the code forms a subset of a finite-field Grassmannian, or equivalently, a subset of the vertices of the corresponding Grassmannian graph....