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ONLINE SPARSE MATRIX GAUSSIAN PROCESS REGRESSION AND VISUAL APPLICATIONS
ONLINE SPARSE MATRIX GAUSSIAN PROCESS REGRESSION AND VISUAL APPLICATIONS
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机译：在线稀疏矩阵高斯过程回归与可视化应用
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摘要
An online sparse matrix Gaussian process (OSMGP) uses online updates to provide an accurate and efficient regression for applications such as pose estimation and object tracking. A regression calculation module calculates a regression on a sequence of input images to generate output predictions based on a learned regression model. The regression model is efficiently updated by representing a covariance matrix of the regression model using a sparse matrix factor (e.g., a Cholesky factor). The sparse matrix factor is maintained and updated in real-time based on the output predictions. Hyperparameter optimization, variable reordering, and matrix downdating techniques can also be applied to further improve the accuracy and/or efficiency of the regression process.
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