Titlebook: Coherence; In Signal Processing David Ramírez,Ignacio Santamaría,Louis Scharf Book 2022 The Editor(s) (if applicable) and The Author(s), un

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Variations on Coherence,rnel methods, and time-frequency modeling. The concept of coherence in compressed sensing and matrix completion is made clear by the restricted isometry property and the concept of coherence index, which are discussed in the chapter. We also consider in this chapter multiview learning, in which the

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Epilogue,it might appear, for likelihood in the MVN model actually leads to the optimization of functions that depend on sums and products of eigenvalues, which are themselves data dependent. Moreover, it is often the case that there is an illuminating Euclidean or Hilbert space geometry. Perhaps it is the g

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Spectral Properties and Regularity,itten as a broadband coherence, with a new definition for broadband coherence. Additionally, this chapter also addresses the problem of testing for block-structured covariance, when the block structure is patterned to model cyclostationarity. Spectral formulas establish the connection with the cyclic spectrum of a cyclostationary time series.

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Fractional Integrals and Semigroups,ry, may provide a way to address vexing problems in signal processing and machine learning, especially when there is no theoretical basis for assigning a distribution to data. This suggestion is developed in more detail in the concluding epilogue to the book.

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Detection of Spatially Correlated Time Series,itten as a broadband coherence, with a new definition for broadband coherence. Additionally, this chapter also addresses the problem of testing for block-structured covariance, when the block structure is patterned to model cyclostationarity. Spectral formulas establish the connection with the cyclic spectrum of a cyclostationary time series.

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Epilogue,ry, may provide a way to address vexing problems in signal processing and machine learning, especially when there is no theoretical basis for assigning a distribution to data. This suggestion is developed in more detail in the concluding epilogue to the book.

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Some Nonlinear Evolution Equations,imension of the measurement space is reduced, under a constraint that distances or dissimilarities between high-dimensional measurements are preserved or approximated in a measurement space of lower dimension.

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Spectral Properties and Regularity,oises. For each noise and signal model, the invariances of the hypothesis testing problem and its GLR are established. Maximum likelihood estimation of unknown signal and noise parameters leads to a variety of coherence statistics.

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Spectral Properties and Regularity,ing the eigenvalues of an average of projection matrices, while the corresponding eigenvectors form a basis for the central subspace. We discuss applications of subspace averaging to subspace clustering and to source enumeration in array processing.