This paper proposes a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of significant interest in many signal processing applications, like analysis of NMR data and system identification. The new algorithm estimates the signal parameters using a matrix pencil constructed from the measured ...

Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is only locally of low-rank, leading t...

We define and study an inference algorithm based on“belief propagation” (BP) and the Bethe approximation. The idea is to encode into a graph an a priori information composed of correlations or marginal probabilities of variables, and to use a message passing procedure to estimate the actual state from some extra realtime information. This method is originally designed for traffic prediction and...

The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel techniques and presents an on-line algorithm which performs nearly as well as any oblivious kernel predictor. The paper contains the derivation of an estimate on the performance of this algorithm. The estimate is then used to d...

background: the aim of this work was to study the feasibility of constructing a fast thorax model suitable for simulating lung motion due to respiration using only one ct dataset. materials and methods: for each of six patients with different thorax sizes, two sets of ct images were obtained in single-breath-hold inhale and exhale stages in the supine position. the ct images were then analyzed ...

Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, kernel distillation, for kernel matrix approximation. The idea adopts from knowledge distillat...

Structured prediction applications often involve complex inference problems that require the use of approximate methods. Approximations based on linear programming (LP) relaxations have proved particularly successful in this setting, with both theoretical and empirical support. Despite the general intractability of inference, it has been observed that in many real-world applications the LP rela...

This paper proposes a novel parametric representation of mandarin intonation based on orthogonal polynomial approximation. The polynomial is a simplified representation of Parallel Encoding and Target Approximation (PENTA) intonation model that includes a target component and an approximation component. We also propose predicting the polynomial parameters from linguistic and phonetic attributes...

This paper demonstrates how the selection of Prediction Strategy is important in the Long-Term Prediction of Time Series. Two strategies are already used in the prediction purposes called Recursive and Direct. This paper presents a third one, DirRec, which combines the advantages of the two already used ones. A simple k -NN approximation method is used and all three strategies are applied to tw...

This paper presents a neural network assisted entry guidance law that is designed by applying Bézier approximation. It is shown that a fully constrained approximation of a reference trajectory can be made by using the Bézier curve. Applying this approximation, an inverse dynamic system for an entry flight is solved to generate guidance command. The guidance solution thus gotten ensures terminal...