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  2. Diffusion map - Wikipedia

    en.wikipedia.org/wiki/Diffusion_map

    Diffusion maps exploit the relationship between heat diffusion and random walk Markov chain.The basic observation is that if we take a random walk on the data, walking to a nearby data-point is more likely than walking to another that is far away.

  3. Montgomery's pair correlation conjecture - Wikipedia

    en.wikipedia.org/wiki/Montgomery's_pair...

    The real line describes the two-point correlation function of the random matrix of type GUE. Blue dots describe the normalized spacings of the non-trivial zeros of Riemann zeta function, the first 10 5 zeros. In the 1980s, motivated by Montgomery's conjecture, Odlyzko began an intensive numerical study of the statistics of the zeros of ζ(s).

  4. Matrix regularization - Wikipedia

    en.wikipedia.org/wiki/Matrix_regularization

    There are a number of matrix norms that act on the singular values of the matrix. Frequently used examples include the Schatten p-norms, with p = 1 or 2. For example, matrix regularization with a Schatten 1-norm, also called the nuclear norm, can be used to enforce sparsity in the spectrum of a matrix.

  5. Normalization (statistics) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(statistics)

    In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment.

  6. Self-similarity matrix - Wikipedia

    en.wikipedia.org/wiki/Self-similarity_matrix

    In data analysis, the self-similarity matrix is a graphical representation of similar sequences in a data series. Similarity can be explained by different measures, like spatial distance ( distance matrix ), correlation , or comparison of local histograms or spectral properties (e.g. IXEGRAM [ 1 ] ).

  7. Covariance and correlation - Wikipedia

    en.wikipedia.org/wiki/Covariance_and_correlation

    With any number of random variables in excess of 1, the variables can be stacked into a random vector whose i th element is the i th random variable. Then the variances and covariances can be placed in a covariance matrix, in which the (i, j) element is the covariance between the i th random variable and the j th one.

  8. Multiple correspondence analysis - Wikipedia

    en.wikipedia.org/wiki/Multiple_correspondence...

    The Burt table is the symmetric matrix of all two-way cross-tabulations between the categorical variables, and has an analogy to the covariance matrix of continuous variables. Analyzing the Burt table is a more natural generalization of simple correspondence analysis , and individuals or the means of groups of individuals can be added as ...

  9. Wishart distribution - Wikipedia

    en.wikipedia.org/wiki/Wishart_distribution

    The Wishart distribution arises as the distribution of the sample covariance matrix for a sample from a multivariate normal distribution. It occurs frequently in likelihood-ratio tests in multivariate statistical analysis. It also arises in the spectral theory of random matrices [citation needed] and in multidimensional Bayesian analysis. [5]