Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II

Mukhopadhyay, Anirban; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra; Coello, Carlos A. Coello

doi:10.1109/tevc.2013.2290082

Cited by 163 publications

(82 citation statements)

References 77 publications

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“…Below we mention the work that we believe to be most directly related to ours, including methods for generalized (not necessarily point-to-point) correspondence and classical biclustering techniques based on probabilistic matrix factorization. For deeper discussions of both areas, the interested reader is referred to [21,25,43].…”

Section: Related Work and Proposed Methodsmentioning

confidence: 99%

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Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering

Denitto

Melzi

Bicego

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

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Region-based correspondence (RBC) is a highly relevant and non-trivial computer vision problem. Given two 3D shapes, RBC seeks segments/regions on these shapes that can be reliably put in correspondence. The problem thus consists both in finding the regions and determining the correspondences between them. This problem statement is similar to that of "biclustering", implying that RBC can be cast as a biclustering problem. Here, we exploit this implication by tackling RBC via a novel biclustering approach, called S 4 B (spatially smooth spike and slab biclustering), which: (i) casts the problem in a probabilistic low-rank matrix factorization perspective; (ii) uses a spike and slab prior to induce sparsity; (iii) is enriched with a spatial smoothness prior, based on geodesic distances, encouraging nearby vertices to belong to the same bicluster. This type of spatial prior cannot be used in classical biclustering techniques. We test the proposed approach on the FAUST dataset, outperforming both state-of-the-art RBC techniques and classical biclustering methods.

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Section: Related Work and Proposed Methodsmentioning

confidence: 99%

“…Biclustering is a well-studied problem, with applications to gene expression data, recommender systems, market segmentation, and other areas [7,8,14,16,25,36]. However, maybe surprisingly, biclustering has not been used for shape correspondence, with the notable exception of [10].…”

Section: Introductionmentioning

confidence: 99%

Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering

Denitto

Melzi

Bicego

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

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“…Multiobjective evolutionary algorithms for DM are surveyed in [23] and [24] into different categories regarding the DM task they face : feature selection, classification, clustering, association rule mining and other tasks. A collection of stages for a general KDD-process is summarized in Figure 1.…”

Section: Floerkemeier Affirm In [2] That Iot Represents a Vision In Wmentioning

confidence: 99%

A knowledge discovery in databases approach for industrial microgrid planning

Gamarra

Guerrero

Montero

2016

Renewable and Sustainable Energy Reviews

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“…Clustering algorithms are categorized into density based [15][16][17][18][19][20], hierarchical [21][22][23][24], partitioning [25,26], model based [27,28], and grid based [29] methods.…”

Section: Introductionmentioning

confidence: 99%