Dimension Reduction for Model-Based Clustering via Mixtures of Multivariate t-Distributions Katherine Morris Advisor University of Guelph, 2012 Prof. Paul D. McNicholas We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of t-distributions. This approach is based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. ...

a r t i c l e i n f o a b s t r a c t This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the mat...

In this manuscript, we formulate the problem of denoising Time Differences of Arrival (TDOAs) in the TDOA space, i.e. the Euclidean space spanned by TDOA measurements. The method consists of pre-processing the TDOAs with the purpose of reducing the measurement noise. The complete set of TDOAs (i.e., TDOAs computed at all microphone pairs) is known to form a redundant set, which lies on a linear...

Decentralized data processing has the benefit of improving wireless monitoring system scalability, reducing the amount of wireless communications, and reducing overall power consumption. In this study, a system identification strategy for single-input multi-output (SIMO) subspace system identification is proposed based on Markov parameters. The method is specifically customized for embedment wi...

Algebraic solvers based on preconditioned Krylov subspace methods are among the most powerful tools for large scale numerical computations in applied mathematics, sciences, technology, as well as in emerging applications in social sciences. As the name suggests, Krylov subspace methods can be viewed as a sequence of projections onto nested subspaces of increasing dimension. They are therefore b...

Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, whose number, orientations, and dimensions are all unknown. In practice one may have access to dimensionality-reduced observations of the data only, resulting, e.g., from undersampling due to complexity and speed constraints on the acquisition device o...

different methods exists for jamming mitigation and we should choose a method based an the jammer type and other parameters. one of the jammers is narrow band fm jammer and we use subspace projection techniques for the suppression of this type of jammer. in subspace projection technique, we estimate the if of signals and construct the subspace vector that is orthogonal to jammer vector by incre...

Let $mathcal{A}$ be a Banach algebra with BAI and $E$ be an introverted subspace of $mathcal{A}^prime$. In this paper we study the quotient Arens regularity of $mathcal{A}$ with respect to $E$ and prove that the group algebra $L^1(G)$ for a locally compact group $G$, is quotient Arens regular with respect to certain introverted subspace $E$ of $L^infty(G)$. Some related result are given as well.

We describe a method for computing the controllable subspace of a linear periodic discrete time system. The method is based on the ordered periodic Schur form 1] by reducing the state equation to a convenient form in which the controllable/uncontrollable states are clearly displayed. Its attractive features are simplicity, numerical accuracy and stability.