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Register a new userKahler information manifolds of signal processing filters in weighted Hardy spaces
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Added by
Jaehyung Choi
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Authors
Jaehyung Choi
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We generalize Kahler information manifolds of complex-valued signal processing filters by introducing weighted Hardy spaces and generic composite functions of transfer functions. We prove that the Riemannian geometry induced from weighted Hardy norms for composite functions of its transfer function is the Kahler manifold. Additionally, the Kahler potential of the linear system geometry corresponds to the square of the weighted Hardy norms for composite functions of its transfer function. By using the properties of Kahler manifolds, it is possible to compute various geometric objects on the manifolds from arbitrary weight vectors in much simpler ways. Additionally, Kahler information manifolds of signal filters in weighted Hardy spaces can generate various information manifolds such as Kahlerian information geometries from the unweighted complex cepstrum or the unweighted power cepstrum, the geometry of the weighted stationarity filters, and mutual information geometry under the unified framework. We also cover several examples from time series models of which metric tensor, Levi-Civita connection, and Kahler potentials are represented with polylogarithm of poles and zeros from the transfer functions when the weight vectors are in terms of polynomials.
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