In this paper, we propose and investigate into several algorithms for finding the suboptimal subspace to represent the signals, and present the error analysis ...
Suboptimal selecting subspace for biorthonormal signal representation. Profile image of Min-Hung Yeh Min-Hung Yeh. 1997, Signal Processing. visibility.
May 27, 2014 · Abstract. We describe ways to define and calculate L1-norm signal subspaces which are less sensitive to outlying data.
Fast recursive orthogonal iteration subspace tracking algorithms and applications. ... Suboptimal selecting subspace for biorthonormal signal representation. 321 ...
Suboptimal selecting subspace for biorthonormal signal representation ... Biorthogonal representations have been widely used in signal processing. Gabor expansion ...
Jul 17, 2020 · Subspaces are low-dimensional, linear portions of the entire signal space that are expected to contain (or be close to) a large part of the observable and ...
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Clearly the simplest selection for C(k) is the instantaneous estimate x(k)xT (k), which gives rise to the Data Projection Method (DPM) first introduced in [69] ...
To obtain a sparse representation with a nonlinear approximation,we choose a new orthonormal basis b⫽{gm[n]}m∈⌫ of CN , which concentrates the signal energy as.
In this paper, we will only be concerned with linear state space models, and we will require that all signals (input, output, and state signals) belong to ...
A fundamental problem is to find the closest point in a fixed sub- space to a given signal. If we have an orthobasis for this subspace, this problem is easy to ...