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22 relations: Compressed sensing, Convex optimization, Diagonal matrix, Gauss–Newton algorithm, Generalized linear model, Geometric median, Huber loss, Iterative method, Least absolute deviations, Least squares, Levenberg–Marquardt algorithm, Linear programming, Linear regression, Loss function, Lp space, M-estimator, Maximum likelihood estimation, Regularization (mathematics), Restricted isometry property, Robust regression, Taxicab geometry, Worcester Polytechnic Institute.
Compressed sensing
Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems.
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Convex optimization
Convex optimization is a subfield of optimization that studies the problem of minimizing convex functions over convex sets.
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Diagonal matrix
In linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero.
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Gauss–Newton algorithm
The Gauss–Newton algorithm is used to solve non-linear least squares problems.
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Generalized linear model
In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.
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Geometric median
The geometric median of a discrete set of sample points in a Euclidean space is the point minimizing the sum of distances to the sample points.
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Huber loss
In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss.
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Iterative method
In computational mathematics, an iterative method is a mathematical procedure that uses an initial guess to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones.
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Least absolute deviations
Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute value (LAV), least absolute residual (LAR), sum of absolute deviations, or the ''L''1 norm condition, is a statistical optimality criterion and the statistical optimization technique that relies on it.
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Least squares
The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns.
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Levenberg–Marquardt algorithm
In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems.
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Linear programming
Linear programming (LP, also called linear optimization) is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships.
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Linear regression
In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).
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Loss function
In mathematical optimization, statistics, econometrics, decision theory, machine learning and computational neuroscience, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.
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Lp space
In mathematics, the Lp spaces are function spaces defined using a natural generalization of the ''p''-norm for finite-dimensional vector spaces.
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M-estimator
In statistics, M-estimators are a broad class of estimators, which are obtained as the minima of sums of functions of the data.
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Maximum likelihood estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model, given observations.
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Regularization (mathematics)
In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.
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Restricted isometry property
In linear algebra, the restricted isometry property (RIP) characterizes matrices which are nearly orthonormal, at least when operating on sparse vectors.
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Robust regression
In robust statistics, robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods.
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Taxicab geometry
A taxicab geometry is a form of geometry in which the usual distance function or metric of Euclidean geometry is replaced by a new metric in which the distance between two points is the sum of the absolute differences of their Cartesian coordinates.
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Worcester Polytechnic Institute
Worcester Polytechnic Institute (WPI) is a private research university in Worcester, Massachusetts, focusing on the instruction and research of technical arts and applied sciences.
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References
[1] https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares
