miscellaneous-10

Image/Document Clustering

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in imagedocument-clustering-20

Trend

Dataset

Best Model

Actions

pendigits

Wine

pixraw10P

Libraries

i

Use these libraries to find imagedocument-clustering-20 models and implementations

Li-Hongmin/MyPaperWithCode

3 papers 21

Datasets

Subtasks

Self-Organized Clustering

Most implemented papers

Robust Graph Learning From Noisy Data

S. Hoi, Zenglin Xu, Zhao Kang, Haiqi Pan•Sun Dec 16 2018

This paper proposes a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data and shows that the proposed model outperforms the previous state-of-the-art methods.

281

Content

iris

warpPIE10P

JAFFE

australian

BA

warpPIE10P

BA

0

Paper Graph

Scalable Spectral Clustering Using Random Binning Features

C. Aggarwal, Pin-Yu Chen, Fangli Xu, Lingfei Wu, I. E. Yen, Yinglong Xia•Thu May 24 2018

This paper presents a novel scalable spectral clustering method using Random Binning features (RB) to simultaneously accelerate both similarity graph construction and the eigendecomposition and introduces a state-of-the-art SVD solver to effectively compute eigenvectors of a large sparse feature matrix generated by RB.

42 0

Paper Graph

An Internal Validity Index Based on Density-Involved Distance

Lianyu Hu, Caiming Zhong•Thu Mar 21 2019

A new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed, which can cope with both the spherical and non-spherical structure of clusters.

40 0

Paper Graph

Ensemble Learning for Spectral Clustering

Hongmin Li, Xiucai Ye, A. Imakura, T. Sakurai•Sat Oct 31 2020

Ensemble clustering has attracted much attention in machine learning and data mining for the high performance in the task of clustering. Spectral clustering is one of the most popular clustering methods and has superior performance compared with the traditional clustering methods. Existing ensemble clustering methods usually directly use the clustering results of the base clustering algorithms for ensemble learning, which cannot make good use of the intrinsic data structures explored by the graph Laplacians in spectral clustering, thus cannot obtain the desired clustering result. In this paper, we propose a new ensemble learning method for spectral clustering-based clustering algorithms. Instead of directly using the clustering results obtained from each base spectral clustering algorithm, the proposed method learns a robust presentation of graph Laplacian by ensemble learning from the spectral embedding of each base spectral clustering algorithm. Finally, the proposed method applies k-means on the spectral embedding obtain from the learned graph Laplacian to get clusters. Experimental results on both synthetic and real-world datasets show that the proposed method outperforms other existing ensemble clustering methods.

26 0

Paper Graph

Divide-and-conquer based Large-Scale Spectral Clustering

Hongmin Li, Xiucai Ye, A. Imakura, T. Sakurai•Thu Apr 29 2021

In the proposed method, a divide-and-conquer based landmark selection algorithm and a novel approximate similarity matrix approach are designed to construct a sparse similarity matrix within low computational complexities and achieves the lower computational complexity than most existing large-scale spectral clustering methods.

22 0

Paper Graph

Adding a benchmark result helps the community track progress.