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Lu et al., 2023 - Google Patents

Robust and scalable Gaussian process regression and its applications

Lu et al., 2023

View PDF
Document ID
16635954011272680973
Author
Lu Y
Ma J
Fang L
Tian X
Jiang J
Publication year
Publication venue
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

External Links

Snippet

This paper introduces a robust and scalable Gaussian process regression (GPR) model via variational learning. This enables the application of Gaussian processes to a wide range of real data, which are often large-scale and contaminated by outliers. Towards this end, we …
Continue reading at openaccess.thecvf.com (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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    • G06K9/6228—Selecting the most significant subset of features
    • G—PHYSICS
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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