The higher rank numerical ranges of generic matrices are described in terms the components their Kippenhahn curves. Cases tridiagonal (in particular, reciprocal) 2-periodic treated more detail.

Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix...

This paper presents a novel adaptive reducedrank multi-input-multi-output (MIMO) decision feedback equalization structure based on joint iterative optimization of adaptive estimators. The novel reduced-rank equalization structure consists of a joint iterative optimization of two equalization stages, namely, a projection matrix that performs dimensionality reduction and a reduced-rank estimator ...

Robust low-rank modelling has recently emerged as a family of powerful methods for recovering the low-dimensional structure of grossly corrupted data, and has become successful in a wide range of applications in signal processing and computer vision. In principle, robust low-rank modelling focuses on decomposing a given data matrix into a low-rank and a sparse component, by minimising the rank ...

The matrix completion problem is to recover a low-rank matrix from a subset of its entries. The main solution strategy for this problem has been based on nuclear-norm minimization which requires computing singular value decompositions – a task that is increasingly costly as matrix sizes and ranks increase. To improve the capacity of solving large-scale problems, we propose a low-rank factorizat...

This paper provides new versions of necessary and sufficient conditions for compatibility of finite discrete conditional distributions, and of the uniqueness for those compatible conditional distributions. We note that the ratio matrix (the matrix C in Arnold and Press (1989)), after interchanging its rows and/or columns, can be rearranged to be an irreducible block diagonal matrix. We find tha...

LetW (A) andWe(A) be the joint numerical range and the joint essential numerical range of an m-tuple of self-adjoint operators A = (A1, . . . , Am) acting on an infinite dimensional Hilbert space, respectively. In this paper, it is shown that We(A) is always convex and admits many equivalent formulations. In particular, for any fixed i ∈ {1, . . . ,m}, We(A) can be obtained as the intersection ...

aim: abnormal joint mobility is an important factor in movement dysfunction and physical disability. a general lack of descriptive details exists for measurements of hip rotation range of motion (rom). this study was designed to establish the influence of hip position on active and passive range of motion of the hip in external and internal rotation(ext & int rot). material & methods: ...

Slow feature analysis (SFA) is a method for extracting slowly varying features from a quickly varying multidimensional signal. An open source Matlab-implementation sfa-tk makes SFA easily useable. We show here that under certain circumstances, namely when the covariance matrix of the nonlinearly expanded data does not have full rank, this implementation runs into numerical instabilities. We pro...