Incremental Robust Nonnegative Matrix Factorization for Object Tracking

Liu, Fanghui; Liu, Mingna; Zhou, Tao; Qiao, Yu; Yang, Jie

doi:10.1007/978-3-319-46672-9_68

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Cited by 6 publications

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“…It has been widely used in face Communicated by Antonio José Silva Neto. (Guillamet and Vitrià 2002), text clustering (Yoo and Choi 2008;Cai et al 2011), speech processing (King et al 2012), blind source separation (Cichocki et al 2006), image representation (Liu et al 2012;Cui et al 2015), and object tracking (Liu et al 2016) etc.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal incremental non-negative matrix factorization algorithm and its application in image classification

Luo

Liu

2020

Comp. Appl. Math.

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To improve the sparseness of the base matrix in incremental non-negative matrix factorization, we in this paper present a new method, orthogonal incremental non-negative matrix factorization algorithm (OINMF), which combines the orthogonality constraint with incremental learning. OINMF adopts batch update in the process of incremental learning, and its iterative formulae are obtained using the gradient on the Stiefel manifold. The experiments on image classification show that the proposed method achieves much better sparseness and orthogonality, while retaining time efficiency of incremental learning.

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