Document clustering by concept factorization

By Wei Xu, Yihong Gong. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004

334 citations recorded by the source corpus.

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Locally Consistent Concept Factorization for Document Clustering (2011, methodology): “In CF, each basis uk is required to be a nonnegative linear combination of the sample vectors xj uk ¼ XN j¼1 xjwjk; ð4Þ where wjk 0.”
Document clustering using locality preserving indexing (2005, methodology): “It has received a lot of attention in recent years [18], [28], [27], [17], [24].”
Non-negative Matrix Factorization on Manifold (2008, background): “Xu & Gong [28] proposed a Concept Factorization approach in which the input data matrix is factorized into three matrix X ≈ XWVT .”
Multi-view clustering: A survey (2018, neutral): “Furthermore, graph regularized MvC approaches via concept factorization[153] were discussed in Refs.”
Constrained Nonnegative Matrix Factorization for Image Representation (2012, neutral): “Concept Factorization-based clustering (CF) [36].”
A Survey of Text Clustering Algorithms (2012, methodology): “An analogous technique called concept factorization was proposed in [98], which can also be applied to data points with negative values in them.”
Nonnegative Matrix Factorization: A Comprehensive Review (2013, methodology): “The similar work was also recommended as Concept Factorization [68].”