IEEE Trans Image Process - Online sparse Gaussian process regression and its applications.

Tópicos

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Resumo

We present a new Gaussian process (GP) inference algorithm, called online sparse matrix Gaussian processes (OSMGP), and demonstrate its merits by applying it to the problems of head pose estimation and visual tracking. The OSMGP is based upon the observation that for kernels with local support, the Gram matrix is typically sparse. Maintaining and updating the sparse Cholesky factor of the Gram matrix can be done efficiently using Givens rotations. This leads to an exact, online algorithm whose update time scales linearly with the size of the Gram matrix. Further, we provide a method for constant time operation of the OSMGP using matrix downdates. The downdates maintain the Cholesky factor at a constant size by removing certain rows and columns corresponding to discarded training examples. We demonstrate that, using these matrix downdates, online hyperparameter estimation can be included at cost linear in the number of total training examples. We describe a robust appearance-based head pose estimation system based upon the OSMGP. Numerous experiments and comparisons with existing methods using a large dataset system demonstrate the efficiency and accuracy of our system. Further, to showcase the applicability of OSMGP to a wide variety of problems, we also describe a regression-based visual tracking method. Experiments show that our OSMGP algorithm generalizes well using online learning.

Resumo Limpo

present new gaussian process gp infer algorithm call onlin spars matrix gaussian process osmgp demonstr merit appli problem head pose estim visual track osmgp base upon observ kernel local support gram matrix typic spars maintain updat spars choleski factor gram matrix can done effici use given rotat lead exact onlin algorithm whose updat time scale linear size gram matrix provid method constant time oper osmgp use matrix downdat downdat maintain choleski factor constant size remov certain row column correspond discard train exampl demonstr use matrix downdat onlin hyperparamet estim can includ cost linear number total train exampl describ robust appearancebas head pose estim system base upon osmgp numer experi comparison exist method use larg dataset system demonstr effici accuraci system showcas applic osmgp wide varieti problem also describ regressionbas visual track method experi show osmgp algorithm general well use onlin learn

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