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Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation
Related concepts (27)
Linear regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Principal component analysis
Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.
Hearing conservation program
Hearing conservation programs are designed to prevent hearing loss due to noise. Hearing conservation programs require knowledge about risk factors such as noise and ototoxicity, hearing, hearing loss, protective measures to prevent hearing loss at home, in school, at work, in the military and, and at social/recreational events, and legislative requirements.
High-dimensional statistics
In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than typically considered in classical multivariate analysis. The area arose owing to the emergence of many modern data sets in which the dimension of the data vectors may be comparable to, or even larger than, the sample size, so that justification for the use of traditional techniques, often based on asymptotic arguments with the dimension held fixed as the sample size increased, was lacking.
Minimum mean square error
In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE), which is a common measure of estimator quality, of the fitted values of a dependent variable. In the Bayesian setting, the term MMSE more specifically refers to estimation with quadratic loss function. In such case, the MMSE estimator is given by the posterior mean of the parameter to be estimated.
Bariatric surgery
Bariatric surgery (or weight loss surgery) is the medical term for a variety of procedures dealing with obesity. Long term weight loss through the standard of care procedures (Roux en-Y bypass, sleeve gastrectomy, and biliopancreatic diversion with duodenal switch) is largely achieved by altering gut hormone levels responsible for hunger and satiety, leading to a new hormonal weight set point. Bariatric surgery is the most effective treatment causing weight loss and reducing complications of obesity.
Kalman filter
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.