Locally Consistent Concept Factorization for Document Clustering

By Deng Cai, Xiaofei He, Jiawei Han. IEEE Transactions on Knowledge and Data Engineering, 2011

379 citations recorded by the source corpus.

Previous studies have demonstrated that document clustering performance can be improved significantly in lower dimensional linear subspaces. Recently, matrix factorization-based techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new approach to extract the document concepts which are consistent with the manifold geometry such that each concept corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the document submanifold. Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document-to-concept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters-21578 have shown that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and mutual information.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f9cd56e04d4901fbccb092d5773c3b16ecdc5b0d

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Citations in the corpus

Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering (2016, methodology): “4) Locally Consistent Concept Factorization (LCCF) (Cai et al., 2011): LCCF is based on NMF with a graph Laplacian regularization and is considered state-of-the-art for document clustering.”
SpectralNet: Spectral Clustering using Deep Neural Networks (2018, methodology): “For completeness, we define ACC and NMI below, and refer the reader to (Cai et al., 2011) for more details.”
Clustering with Deep Learning: Taxonomy and New Methods (2018, methodology): “As evaluation metric we use clustering accuracy (ACC) and normalized mutual information (NMI) (Strehl and Ghosh, 2002; Vinh et al., 2010; Cai et al., 2011).”
Graph Regularized Sparse Coding for Image Representation (2011, background): “However, they all fail to consider the geometrical structure in the data, which has been demonstrated the powerful discriminant ability in many applications [29], [30].”