Reduced rank regression analysis provides maximum likelihood estimators of a matrix of regression coefficients of specified rank and of corresponding linear restrictions on such matrices. These estimators depend on the eigenvectors of an ‘‘effect’’ matrix in the metric of an error covariance matrix. In this paper it is shown that the maximum likelihood estimator of the restrictions can be appro...

â â  the present study attempts to investigate the urban hierarchy in iran between 1996-2006. the research method is content and statistical analysis. the necessity data were collected by documental method. in addition to the analysis and sorting of the data regarding the situation of iranian cities based on different models such as entropy ,ginny coefficients, urban concentration index, and ra...

The minimal polynomial extrapolation (MPE) and reduced rank extrapolation (RRE) are two very effective techniques that have been used in accelerating the convergence of vector sequences, such as those that are obtained from iterative solution of linear and nonlinear systems of equations. Their definitions involve some linear least squares problems, and this causes difficulties in their numerica...

We show that the reduced-rank output signal computed via truncated (Q)SVD is identical to that from an array of parallelly connected analysis-synthesis finite impulse response (FIR) filter pairs. The filter coefficients are determined by the (Q)SVD, and the filters provide an explicit description of the reduced-rank noise reduction algorithm in the frequency domain.

In the last few years, Facial Expression Synthesis (FES) has been a flourishing area of research driven by applications in character animation, computer games, and human computer interaction. This paper proposes a photorealistic FES method based on Bilinear Kernel Reduced Rank Regression (BKRRR). BKRRR learns a high-dimensional mapping between the appearance of a neutral face and a variety of e...

1 ‘Parameter expanded’ and standard expectation maximisation algorithms are de2 scribed for reduced rank estimation of covariance matrices by restricted maximum 3 likelihood, fitting the leading principal components only. Convergence behaviour of 4 these algorithms is examined for several examples and contrasted to that of the aver5 age information algorithm, and implications for practical anal...

Important array signal processing techniques such as Spatial Smoothing, Forward Backward Averaging and RootMUSIC require arrays with precise and specific geometries and responses. However, building sensor arrays with such demanding characteristics is not always possible. To deal with these possible limitations the real array response can be interpolated into the desired response applying array ...

While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent proliferation of a number of cost-effective approximations to GPs, both for classification and for regr...

The Subspace-based Reduced Rank and Polynomial Order (RRPO) methods were proposed recently [1, 2, 3], which estimate a reduced order linear prediction polynomial whose roots are the desired "signal roots". In this paper, we describe how to extend the RRPO methods to include constraints involving known signal information. Simulation results indicate that by incorporating known signal information...