K-means clustering via principal component analysis

By Chris H. Q. Ding, Xiaofeng He. International Conference on Machine Learning, 2004

1,702 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/41809d7fc7c41cf4d0afd5823034b5c0ac2949aa

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Citations in the corpus

Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization (2008, background): “In fact, LSA and NMF give continuous solutions to the same K-means clustering problem [ 7 ].”
Feature-based transfer learning with real-world applications (2010, background): “[220] and Ding and He [55] have proven that the k principal components of a term-document co-occurrence matrix, which are referred to as the k largest eigenvectors u1, u2, .”
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization (2006, methodology): “PCA relates to Kmeans in that the relaxed solution of cluster membership indicators are given by principal components, and the subspace spanned by the cluster centroids are given by the PCA principal subspace (Ding & He, 2004; Zha et al., 2002).”
Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis (2009, background): “A later publication by Ding and He (2004) pointed out the redundancy in Z such that the K-means solution can be defined by the first K−1 eigenvectors.”
Revealing Event Saliency in Unconstrained Video Collection (2017, neutral): “Based on the extracted multi-modality features, we adopt the K-means algorithm [28] to cluster all the video shots in the given video collection into n c clusters and thus obtain {C i }”
Evolutionary spectral clustering by incorporating temporal smoothness (2007, methodology): “They have been successfully applied to many areas such as document clustering [22, 15], imagine segmentation [19, 21], and Web/blog clustering [9, 18].”
Handbook of Cluster Analysis (2015, background): “The relationship with K-means is even deeper than we have presented it here [16].”
Local Residents’ Attitude toward Sustainable Rural Tourism Development (2016, neutral): “Mathematically, this is equivalent to finding the best low rank approximation of the data via the singular value decomposition [51].”
Data Mining for Internet of Things: A Survey (2014, background): “From the bottom up, the five layers are: edge technology, access gateway, internet, middleware, and application.”
Orthogonal nonnegative matrix t-factorizations for clustering (2006, methodology): “In fact, PCA is effectively doing K-means clustering[ 8 , 34].”
Optimization Models for Machine Learning: A Survey (2019, neutral): “1, [85]).”
Machine Learning - The Art and Science of Algorithms that Make Sense of Data (2012, background): “Ding and He (2004) discuss the relationship between K -means clustering and principal component analysis.”
A Semi-NMF-PCA Unified Framework for Data Clustering (2017, methodology): “This is similar to PCA being the spectral relaxation of k -means clustering [12].”
Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach (2015, background): “Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a uni.ed representation of latent features to capture both intra-and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data.”
Clustering with Deep Learning: Taxonomy and New Methods (2018, methodology): “In the past, dimensionality reduction (or representation learning) and clustering have been treated separately, and sequentially applied on the data (Ding and He, 2004; Tian et al., 2014; Trigeorgis et al., 2014).”