Face recognition method, system and storage medium based on kernel non-negative matrix factorization
A non-negative matrix decomposition and face recognition technology, applied in the field of face recognition, can solve problems such as slow convergence speed, inability to adjust power parameters, poor parameter controllability, etc., to ensure reliability, overcome changes in posture and illumination, The effect of fast convergence speed
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[0041] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0042] Terminology Explanation
[0043] 1. Non-negative Matrix Factorization (NMF)
[0044] The basic idea of NMF is to take a non-negative sample matrix Approximate decomposition into the product of two non-negative matrices, namely:
[0045] X≈WH,
[0046] in, and are called the base image matrix and the coefficient (feature) matrix, respectively.
[0047] Kernel Function
[0048] Let χ be the input space, k(·,·) be a symmetric function defined on χ×χ, then k is a kernel function if and only if for any finite data set The following Gram matrix K is always positive semi-definite,
[0049]
[0050] 2. Kernel Non-negative Matrix Factorization (KNMF)
[0051] The basic idea of KNMF is to map the non-negative sample matrix X to a high-dimensional space through a nonlinear mapping φ, so that the mapped s...
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