Add manuscript citation (#10)

CITATION.cff

@@ -0,0 +1,37 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: 'GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization'
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Youdong
+    family-names: Guo
+    orcid: 'https://orcid.org/0009-0007-7787-3722'
+  - given-names: Timothy E.
+    family-names: Holy
+    orcid: 'https://orcid.org/0000-0002-2429-1071'
+identifiers:
+  - type: url
+    value: 'https://arxiv.org/abs/2408.08260'
+    description: The ArXiv deposit of the encompassing paper.
+doi: 'https://doi.org/10.48550/arXiv.2408.08260'
+repository-code: 'https://github.com/HolyLab/GsvdInitialization.jl'
+abstract: >-
+  Non-negative matrix factorization (NMF) is an important
+  tool in signal processing and widely used to separate
+  mixed sources into their components. However, NMF is
+  NP-hard and thus may fail to discover the ideal
+  factorization; moreover, the number of components may not
+  be known in advance and thus features may be missed or
+  incompletely separated. To recover missing components from
+  under-complete NMF, we introduce GSVD-NMF, which proposes
+  new components based on the generalized singular value
+  decomposition (GSVD) between preliminary NMF results and
+  the SVD of the original matrix. Simulation and
+  experimental results demonstrate that GSVD-NMF often
+  recovers missing features from under-complete NMF and
+  helps NMF achieve better local optima.

README.md

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 [![CI](https://github.com/HolyLab/GsvdInitialization.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/HolyLab/GsvdInitialization.jl/actions/workflows/CI.yml)
 [![codecov](https://codecov.io/gh/HolyLab/GsvdInitialization.jl/graph/badge.svg?token=LxqRCsZIvn)](https://codecov.io/gh/HolyLab/GsvdInitialization.jl)
 
-This package implements the technique in the paper 'GSVD-NMF: Recovering Missing Features in
-Non-negative Matrix Factorization`.
+This package implements the technique in the paper [GSVD-NMF: Recovering Missing Features in
+Non-negative Matrix Factorization](https://arxiv.org/abs/2408.08260).
 It is used to recover Non-negative matrix factorization(NMF) components from an initial lower-rank factorization by exploiting the generalized singular value decomposition (GSVD) between existing NMF results and the SVD of X.
 This method allows the incremental expansion of the number of components, which can be convenient and effective for interactive analysis of large-scale data.
 
@@ -145,4 +145,4 @@ Arguments:
 
 ## Citation
 
-If you find this package useful please cite:
+Thanks for citing this work! See the "Cite this repository" link in the "About" bar for format options.