Affinity aggregation for spectral clustering

Huang, Hong; Chuang, Yung-Yu; Chen, Chu-Song

doi:10.1109/cvpr.2012.6247748

Cited by 63 publications

(16 citation statements)

References 27 publications

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“…A trend to use such low dimensional representations of features describing higher dimensional data can be seen in current research of representation learning. Corresponding literature clearly shows the progression from manually selected features [25][26][27] towards modeling the features in a latent space for further processing [28][29][30].…”

Section: Methodsmentioning

confidence: 99%

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

et al. 2021

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Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework for selecting the most diverse candidates from a given larger set of possible light spectra. The spectral information per wavelength is first reduced by applying a convolutional autoencoder. The relevant features are then selected based on Laplacian Scores and transformed to a two-dimensional embedded space for subsequent clustering. The low dimensional embedding, from which the required diversity follows, is done with respect to the locality of the features. In a second step, photometric parameters are considered and a second clustering is performed. As a result of this algorithmic pipeline, the most diverse selection of light spectra complying with a given set of relevant photometric parameters can be extracted and used for further experiments or applications.

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Section: Methodsmentioning

confidence: 99%

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

et al. 2021

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“…this concatenation makes no physical sense, and treats all the views equally and looks past their differences. A great deal of work [2][3][4][5][6][7][8][9][10] has been developed to effectively utilize the information from multiple views, such as Auto-weighted Multi-view Graph Learning (AMGL) [6], Multi-view clustering via Mixed Embedding Approximation (MEA) [7], Multi-view Learning with Adaptive Neighbors (MLAN) [9], Multi-View Graph Learning (MVGL) [10], etc. Nonetheless, there are two drawbacks in these approaches: they focus only on mining the linear correlations among different views but fail to capture more complex relationships; many of them ignore the interdependence between unified embedding and cluster labels, resulting in suboptimal and random clustering results.…”

Section: Introductionmentioning

confidence: 99%

Dependence-Guided Multi-View Clustering

Dong

Nie

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a novel approach called dependenceguided multi-view clustering (DGMC). Our model enhances the dependence between unified embedding learning and clustering, as well as promotes the dependence between unified embedding and embedding of each view. Specifically, DGMC learns a unified embedding and partitions data in a joint fashion, thus the clustering results can be directly obtained. A kernel dependence measure is employed to learn a unified embedding by forcing it to be close to different views, thus the complex dependence among different views can be captured. Moreover, an implicit-weight learning mechanism is provided to ensure the diversity of different views. An efficient algorithm with rigorous convergence analysis is derived to solve the proposed model. Experimental results demonstrate the advantages of the proposed method over the state of the arts on real-world datasets.

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“…The final clustering is obtained by spectral clustering of fusion networks. Some multi-view clustering methods based on spectral clustering have also been proposed (Huang et al, 2012;Kumar et al, 2011;Zhang et al, 2015). They used different integration methods to combine the spectral clustering results from a single view.…”

Section: Introductionmentioning

confidence: 99%

“…They used different integration methods to combine the spectral clustering results from a single view. The Affinity Aggregation for Spectral Clustering (AASC) algorithm (Huang et al, 2012) introduced weights in the spectral clustering of each view, and then added them together to optimize the weights in the calculation.…”

Section: Introductionmentioning

confidence: 99%

Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views

Tian

Zhao

Zheng

2021

Preprint

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Background: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of these datasets has proved to be valuable for biological and medical research and helpful to reveal data structures from multiple collections. However, such data often have small sample size and high dimension. It is difficult to find a suitable integration method for structural analysis of multiple datasets. Results: In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, the optimization problem solved by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same.Conclusion: Particularly, our approach has better performs when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

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Affinity aggregation for spectral clustering

Cited by 63 publications

References 27 publications

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

Dependence-Guided Multi-View Clustering

Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views

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