Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization

Li, Tao; Ding, Chris; Jordan, Michael I.

doi:10.1109/icdm.2007.98

Cited by 162 publications

(111 citation statements)

References 11 publications

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“…By combining the codes generated by independent runs of LSH, CH 1 obtains a remarkable improvement on LSH, and consistently outperforms other state-of-the-art methods on these datasets. This demonstrates that the performance of weak hashing methods can be largely improved by the proposed consensus strategy, which is consistent with the previous conclusions made in classifier combination (Schapire 1990;Breiman 1996;Breiman 2001) and clustering combination (Monti et al 2003;Li et al 2007;Fred and Jain 2005).…”

Section: Results and Analysissupporting

confidence: 80%

“…During the past decades, a great number of consensus clustering approaches have been proposed. Representative works include similarity based methods (Fred and Jain 2005), graph based methods (Fern and Brodley 2004) and non-negative matrix factorization (NMF) based methods (Li et al 2007). Similarity based methods express the base clustering results as similarity matrices and generate the final clustering result based on the consensus similarity matrix (Fred and Jain 2005).…”

Section: Introductionmentioning

confidence: 99%

“…The basic idea of graph based methods is to construct an undirected graph to model the base clustering results, and then derive the ensemble clustering via graph partitioning (Fern and Brodley 2004). Li et al (2007) found that consensus clustering can be formulated within the framework of NMF and proposed a simple iterative algorithm. Recently, weighted consensus clustering (Li and Ding 2008) has been considered in which a weight for each base clustering is introduced to model its importance.…”

Section: Introductionmentioning

confidence: 99%

“…This learning scheme is originally designed for unsupervised tasks such as clustering, i.e., consensus clustering (Monti et al 2003;Li et al 2007;Fred and Jain 2005) Consensus clustering is a kind of ensemble whose base learners are clusters generated by clustering methods. Ensemble of clustering is considerably more difficult than the combination of classifiers, because the cluster labels produced by different clustering methods may not correspond.…”

Section: Introductionmentioning

confidence: 99%

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Consensus hashing

Leng

Cheng

2015

Mach Learn

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Hashing techniques have been widely used in many machine learning applications because of their efficiency in both computation and storage. Although a variety of hashing methods have been proposed, most of them make some implicit assumptions about the statistical or geometrical structure of data. In fact, few hashing algorithms can adequately handle all kinds of data with different structures. When considering hybrid structure datasets, different hashing algorithms might produce different and possibly inconsistent binary codes. Inspired by the successes of classifier combination and clustering ensembles, in this paper, we present a novel combination strategy for multiple hashing results, named consensus hashing. By defining the measure of consensus of two hashing results, we put forward a simple yet effective model to learn consensus hash functions which generate binary codes consistent with the existing ones. Extensive experiments on several large scale benchmarks demonstrate the overall superiority of the proposed method compared with state-of-the-art hashing algorithms.

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Section: Results and Analysissupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Consensus hashing

Leng

Cheng

2015

Mach Learn

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“…• Consensus Clustering: Most consensus clustering algorithms are not designed for our setting, where not all the samples are labeled by experts [94]. However, since the average similarity matrix between samples is available, Cluster-based Similarity Partitioning Algorithm (CSPA)…”

Section: Competing Alternativesmentioning

confidence: 99%

Clustering with flexible constraints and application to disease subtyping

Chang¹

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Cluster Ensembles

Acharya¹,

Ghosh²

2019

Wiley StatsRef: Statistics Reference Online

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Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This article describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learned, while at the same time highlighting the unique features of individual approaches.

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Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization

Cited by 162 publications

References 11 publications

Consensus hashing

Consensus hashing

Clustering with flexible constraints and application to disease subtyping

Cluster Ensembles

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