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}}</ref> However, as in many other data mining applications, a local minimum may still prove to be useful.
In addition to the optimization step, initialization has a significant effect on NMF. The initial values chosen for {{math|'''W'''}} and {{math|'''H'''}} may affect not only the rate of convergence, but also the overall error at convergence. Some options for initialization include complete randomization, [[Singular value decomposition|SVD]], k-means clustering, and more advanced strategies based on these and other paradigms.<ref>{{Cite journal |last=Hafshejani |first=Sajad Fathi |last2=Moaberfard |first2=Zahra |date=November 2022 |title=Initialization for Nonnegative Matrix Factorization: a Comprehensive Review |url=http://arxiv.org/abs/2109.03874 |journal=International Journal of Data Science and Analytics |volume=16 |issue=1 |pages=119–134 |doi=10.1007/s41060-022-00370-9 |issn=2364-415X}}</ref>
[[File:Fractional_Residual_Variances_comparison,_PCA_and_NMF.pdf|thumb|500px|Fractional residual variance (FRV) plots for PCA and sequential NMF;<ref name="ren18"/> for PCA, the theoretical values are the contribution from the residual eigenvalues. In comparison, the FRV curves for PCA reaches a flat plateau where no signal are captured effectively; while the NMF FRV curves are declining continuously, indicating a better ability to capture signal. The FRV curves for NMF also converges to higher levels than PCA, indicating the less-overfitting property of NMF.]]
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