Uncorrelated, or increasing convex, positive data with multiple least-squares exponential curves

On Least Squares Exponential Sum Approximation With Positive Coefficients*

An algorithm is given for finding optimal least squares exponential sum approximations to sampled data subject to the constraint that the coefficients appearing in the exponential sum are positive. The algorithm employs the divided differences of exponentials to overcome certain problems of ill-conditioning and is suitable for data sampled at noninteger times.

Convex Total Least Squares

We study the total least squares (TLS) problem that generalizes least squares regression by allowing measurement errors in both dependent and independent variables. TLS is widely used in applied fields including computer vision, system identification and econometrics. The special case when all dependent and independent variables have the same level of uncorrelated Gaussian noise, known as ordin...

Least-Squares Fitting of Data with B-Spline Curves

Unifying Least Squares, Total Least Squares and Data Least Squares

The standard approaches to solving overdetermined linear systems Ax ≈ b construct minimal corrections to the vector b and/or the matrix A such that the corrected system is compatible. In ordinary least squares (LS) the correction is restricted to b, while in data least squares (DLS) it is restricted to A. In scaled total least squares (Scaled TLS) [15], corrections to both b and A are allowed, ...

Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction

In this paper, a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative power between classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a transformation matrix such that the squared regression error is minimized. To this end, two least squ...