Parallel multi-view concept clustering in distributed computing

Wang, Hao; Yang, Yan; Zhang, Xiaobo; Peng, Bo

doi:10.1007/s00521-019-04243-4

Cited by 10 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Roughly speaking, the existing methods can be divided into two categories. The first class of methods try to fuse the features [12,13], while the others integrate clusterings [14]. Nevertheless, these methods are susceptible to poor quality data, which lead to degraded clustering performance.…”

Section: Introductionmentioning

confidence: 99%

Scalable Multi-view Clustering with Graph Filtering

Liu¹,

Chen²,

Luo³

et al. 2022

Preprint

View full text Add to dashboard Cite

With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Moreover, they are often designed for feature data and ignore the rich topology structure information. Accordingly, in this paper, we propose a generic framework to cluster both attribute and graph data with heterogeneous features. It is capable of exploring the interplay between feature and structure. Specifically, we first adopt graph filtering technique to eliminate high-frequency noise to achieve a clustering-friendly smooth representation. To handle the scalability challenge, we develop a novel sampling strategy to improve the quality of anchors. Extensive experiments on attribute and graph benchmarks demonstrate the superiority of our approach with respect to state-of-the-art approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Scalable Multi-view Clustering with Graph Filtering

Liu¹,

Chen²,

Luo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Specifically, ODEFS uses a given outlier scoring method to compute initial outlier scores of data objects, and then defines an outlier thresholding function to identify a set of outlier candidates. Considering that diverse outliers may have different discriminative feature subsets (Wang et al 2019b;Wang and Li 2006), ODEFS builds an ensemble framework to obtain multiple feature subsets by bagging. It randomly chooses examples from both outlier candidates and the unlabeled objects.…”

Section: Introductionmentioning

confidence: 99%

Outlier Detection Ensemble with Embedded Feature Selection

Cheng¹,

Wang²,

Liu³

et al. 2020

AAAI

View full text Add to dashboard Cite

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS.

show abstract

Multi-view clustering with dual tensors

Ren

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Parallel multi-view concept clustering in distributed computing

Cited by 10 publications

References 39 publications

Scalable Multi-view Clustering with Graph Filtering

Scalable Multi-view Clustering with Graph Filtering

Outlier Detection Ensemble with Embedded Feature Selection

Multi-view clustering with dual tensors

Contact Info

Product

Resources

About