欢乐中国
发表于 2025-3-23 10:22:35
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逃避责任
发表于 2025-3-23 14:08:47
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Aggregate
发表于 2025-3-23 22:01:49
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Ornament
发表于 2025-3-24 02:04:25
Introduction,eir applications in many disciplines in science and engineering. The practical applications of SPD matrices are numerous, including Diffusion Tensor Imaging (DTI) in brain imaging , kernel learning in machine learning, radar signal processing , and Brain Computer Interface (BCI) applications .
Presbyopia
发表于 2025-3-24 03:54:47
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hankering
发表于 2025-3-24 08:06:55
Data Representation by Covariance Operatorsis chapter, by employing the feature map viewpoint of kernel methods in machine learning, we generalize covariance matrices to infinite-dimensional covariance operators in RKHS. Since they encode . between input features, they can be employed as a powerful form of data representation, which we explore in subsequent chapters.
吹气
发表于 2025-3-24 13:57:09
Geometry of Covariance Operatorsrators. These distances and divergences can then be directly employed in a practical application, e.g., image classification. We emphasize, however, that the concepts we present below are general and applicable in any application involving the comparison of covariance operators.
Alveolar-Bone
发表于 2025-3-24 18:20:01
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maudtin
发表于 2025-3-24 22:26:56
We then present a statistical interpretation of this framework, which shows that assuming that an image can be represented by a covariance matrix is essentially equivalent to assuming that its features are random variables generated by a multivariate Gaussian probability distribution with mean zero
防锈
发表于 2025-3-25 01:49:59
d images by covariance matrices, this means that we need to have a similarity measure between covariance matrices. Since covariance matrices, properly regularized if necessary, are symmetric, positive definite (SPD matrices), a natural approach to measuring their similarity is via a distance (or dis