Constrained Spectral Clustering via Exhaustive and Efficient Constraint Propagation

Lu, Zhiwu; Ip, Horace H. S.

doi:10.1007/978-3-642-15567-3_1

Cited by 57 publications

(58 citation statements)

References 15 publications

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“…To alleviate this limitation, pairwise constraints can be propagated to gain many soft constraints, based on the constraint-level local smoothness assumption. [16] presents a closed-form propagation method, as follows:…”

Section: The Neighbourhood System Vmentioning

confidence: 99%

“…It has been proven that |V pc ij | ≤ 1 [16]. It is considered that there is a soft constraint between x i and x j : W * ij > 0 means a soft must-link; V pc ij < 0 means a soft cannot-link.…”

Section: The Neighbourhood System Vmentioning

confidence: 99%

“…Together with the pairwise constraints, the local smoothness assumptions are also incorporated into the model to achieve the robust performance. [16]. The proposed model is optimized by the simulated field algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

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Constrained Clustering and Its Application to Face Clustering in Videos

Zhang

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

106

126

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Section: The Neighbourhood System Vmentioning

confidence: 99%

Section: The Neighbourhood System Vmentioning

confidence: 99%

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Constrained Clustering and Its Application to Face Clustering in Videos

Zhang

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

106

126

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“…People wish to resort the massive unconstrained data to help propagate the scarce pairwise constraints, in other words, to solve the challenging problem of pairwise constraint propagation based on the traditional graph-based SSL framework. For instance, Lu and Ip propose an exhaustive and efficient constraint propagation method (E 2 CP), which decomposes the constraint propagation problem as a series of independent label propagation (SSL) subproblems [14].…”

Section: Introductionmentioning

confidence: 99%

Local similarity learning for pairwise constraint propagation

et al. 2014

Multimed Tools Appl

Self Cite

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Pairwise constraint propagation studies the problem of propagating the scarce pairwise constraints across the entire dataset. Effective propagation algorithms have previously been designed based on the graph-based semi-supervised learning framework. Therefore, these previous constraint propagation methods rely critically on a good similarity measure over the data points. Improper or noisy similarity measurements may dramatically degrade the performance of the constraint propagation algorithms. In this paper, we make attempt to exploit the available pairwise constraints to learn a new set of similarities, which are consistent with the supervisory information in the pairwise constraints, before propagating these initial constraints. Our method is a local learning algorithm. More specifically, we compute the similarities at each data point through simultaneously minimizing the local reconstruction error and local constraint error. The proposed method has been tested in the constrained clustering tasks on eight real-life datasets and then shown to achieve significant improvements with respect to the state of the arts.

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“…However, although elegant, such a naive realization of the concept leads to only limited local geometric impact, and requires many constraints to be placed before a significant effect takes place. Some attempts have been made at improving this method by propagating the constraints themselves to nearby pairs [13,26,27]. Other methods propagate the constrained entries in the affinity matrix to nearby entries [14].…”

Section: Introductionmentioning

confidence: 99%

Constraints as Features

Asafi

Cohen–Or

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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In this paper, we introduce a new approach to constrained clustering which treats the constraints as features. Our method augments the original feature space with additional dimensions, each of which derived from a given Cannot-link constraints. The specified Cannot-link pair gets extreme coordinates values, and the rest of the points get coordinate values that express their spatial influence from the specified constrained pair. After augmenting all the new features, a standard unconstrained clustering algorithm can be performed, like k-means or spectral clustering. We demonstrate the efficacy of our method for active semi-supervised learning applied to image segmentation and compare it to alternative methods. We also evaluate the performance of our method on the four most commonly evaluated datasets from the UCI machine learning repository.

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Constrained Spectral Clustering via Exhaustive and Efficient Constraint Propagation

Cited by 57 publications

References 15 publications

Constrained Clustering and Its Application to Face Clustering in Videos

Constrained Clustering and Its Application to Face Clustering in Videos

Local similarity learning for pairwise constraint propagation

Constraints as Features

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