Two dimensional correlation analysis is a mathematical technique that is used to study changes in measured signals. As mostly spectroscopic signals are...

Two-Dimensional Nuclear Magnetic Resonance (2D NMR) is an advanced spectroscopic technique that builds upon the capabilities of one-dimensional (1D) NMR...

a property known as dimensional homogeneity. Checking for dimensional homogeneity is a common application of dimensional analysis, serving as a plausibility...

traditional animation Two-dimensional correlation analysis 2d abbreviation of second, the ordinal numeral corresponding to two 2-D (character), a member of the...

degrees of freedom and their change upon water reorganization. Two-dimensional correlation analysis P. Hamm; M. H. Lim; R. M. Hochstrasser (1998). "Structure...

scale. The main result of this technique is a two-dimensional absorption spectrum that shows the correlation between excitation and detection frequencies...

produce three-dimensional data, the Fourier shell correlation (FSC) measures the normalised cross-correlation coefficient between two 3-dimensional volumes...

multivariate analysis of variance (MANOVA) is a procedure for comparing multivariate sample means. As a multivariate procedure, it is used when there are two or...

statistics, Spearman's rank correlation coefficient or Spearman's ρ is a number ranging from -1 to 1 that indicates how strongly two sets of ranks are correlated...

canonical-correlation analysis (CCA), also called canonical variates analysis, is a way of inferring information from cross-covariance matrices. If we have two...

Autocorrelation, sometimes known as serial correlation in the discrete time case, measures the correlation of a signal with a delayed copy of itself....

related to canonical correlation analysis (CCA). CCA defines coordinate systems that optimally describe the cross-covariance between two datasets while PCA...

"error" terms, hence factor analysis can be thought of as a special case of errors-in-variables models. The correlation between a variable and a given...

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the...

In probability theory and statistics, partial correlation measures the degree of association between two random variables, with the effect of a set of...

biological networks based on pairwise correlations between variables. While it can be applied to most high-dimensional data sets, it has been most widely...

particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions...

Two-dimensional losses are easily evaluated using Navier-Stokes equations, but three-dimensional losses are difficult to evaluate; so, correlation is...

statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the...

typically represented as two-dimensional (2D) arrays of discrete signal samples. If we rearrange the signal samples into a one-dimensional (1D) vector, then...

correlation analysis (gCCA), is a way of making sense of cross-correlation matrices between the sets of random variables when there are more than two...

distance correlation or distance covariance is a measure of dependence between two paired random vectors of arbitrary, not necessarily equal, dimension. The...

a number called the scaling dimension of O {\displaystyle O} . This implies in particular that the two point correlation function ⟨ O ( x ) O ( 0 ) ⟩...

sphere. A two-dimensional Euclidean space is a two-dimensional space on the plane. The inside of a cube, a cylinder or a sphere is three-dimensional (3D) because...

Multivariate Analysis. 82 (2): 299–330. doi:10.1006/jmva.2001.2034. Haghighat, M.; Abdel-Mottaleb, M.; Alhalabi, W. (2016). "Discriminant Correlation Analysis: Real-Time...

distance functions problematic in high-dimensional spaces. This led to new clustering algorithms for high-dimensional data that focus on subspace clustering...

high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. The expression...

doi:10.1088/0953-4075/46/16/164028 Noda, I. (1993). "Generalized two-dimensional correlation method applicable to infrared, Raman, and other types of spectroscopy"...

dimensions, N, an MDS algorithm places each object into N-dimensional space (a lower-dimensional representation) such that the between-object distances are...

problem of partitioning data points into groups based on their similarity. Correlation clustering provides a method for clustering a set of objects into the...