This paper describes a technique for performing intra prediction of the chroma planes based on the reconstructed luma plane in the frequency domain. This prediction exploits the fact that while RGB to YUV color conversion has the property that it decorrelates the color planes globally across an image, there is still some correlation locally at the block level. Previous proposals compute a linea...

Predictor polynomials are often used in linear prediction methods mainly for extracting properties of physical systems which are described by time series. The aforementioned properties are associated with a few zeros of large polynomials and for this reason the zero locations of those polynomials must be analyzed. We present a linear algebra approach for determining the zero locations of predic...

We propose a uniied analysis for a class of infeasible-start predictor-corrector algorithms for semideenite programming problems, using the Monteiro-Zhang uniied direction. The algorithms are direct generalizations of the Mizuno-Todd-Ye predictor-corrector algorithm for linear programming. We show that the algorithms belonging to this class are globally convergent, provided the problem has a so...

We establishes the polynomial convergence of a new class of pathfollowing methods for semidefinite linear complementarity problems, whose search directions belong to the class of directions introduced by Monteiro [9]. Namely, we show that the polynomial iteration-complexity bound of the well known algorithms for linear programming, namely the predictor-corrector algorithm of Mizuno and Ye, carr...

This paper concerns the intraframe coding of pictures. Conventionally the predictive coding methods use a two dimensional linear predictor which is based on the autoregressive model of the picture. This paper proposes two versions of a twodimensional lattice algorithm for linear prediction. This algorithm is arrived at by representing the forward prediction error as a function of a lower order ...

In low bit-rate coders, the near-sample and far-sample redundancies of the speech signal are usually removed by a cascade of a shortterm and a long-term linear predictor. These two predictors are usually found in a sequential and therefore suboptimal approach. In this paper we propose an analysis model that jointly finds the two predictors by adding a regularization term in the minimization pro...

We propose a novel semi-supervised structured output prediction method based on local linear regression in this paper. The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction. To solve this problem, we ...

A theory for linear multistep schemes applied to the initial value problem for a nonlinear first order system of differential equations with a singularity of the first kind is developed. Predictor-corrector schemes are also considered. The specific examples given are systems derived from partial differential equations in the presence of symmetry.

This paper generalizes the methods developed in Shannon, Province and Rao (2001) to use recursive partitioning to identify subsets of the aggregate data within each of which simple linear regression models give better fit. This method is proposed as an alternative to multivariate regression modeling when the analyst is primarily concerned with the regression of an outcome onto a single predicto...

Partial Factor Modeling: Predictor-Dependent Shrinkage for Linear Regression P. Richard Hahn a , Carlos M. Carvalho b & Sayan Mukherjee c a Booth School of Business , University of Chicago , Chicago , IL , 60637 b McCombs School of Business , The University of Texas , Austin , TX , 78712 c Departments of Statistical Science, Computer Science, Mathematics, and Institute for Genome Sciences Polic...