This content will become publicly available on March 6, 2025
- PAR ID:
- 10525022
- Editor(s):
- Beirami, Ahmed
- Publisher / Repository:
- Openreview
- Date Published:
- Journal Name:
- Transactions on machine learning research
- ISSN:
- 2835-8856
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Multi-View Clustering (MVC) aims to find the cluster structure shared by multiple views of a particular dataset. Existing MVC methods mainly integrate the raw data from different views, while ignoring the high-level information. Thus, their performance may degrade due to the conflict between heterogeneous features and the noises existing in each individual view. To overcome this problem, we propose a novel Multi-View Ensemble Clustering (MVEC) framework to solve MVC in an Ensemble Clustering (EC) way, which generates Basic Partitions (BPs) for each view individually and seeks for a consensus partition among all the BPs. By this means, we naturally leverage the complementary information of multi-view data in the same partition space. Instead of directly fusing BPs, we employ the low-rank and sparse decomposition to explicitly consider the connection between different views and detect the noises in each view. Moreover, the spectral ensemble clustering task is also involved by our framework with a carefully designed constraint, making MVEC a unified optimization framework to achieve the final consensus partition. Experimental results on six real-world datasets show the efficacy of our approach compared with both MVC and EC methods.
-
One significant challenge in the field of supervised deep learning is the lack of large-scale labeled datasets for many problems. In this paper, we propose Consensus Spectral Clustering (CSC), which leverages the strengths of convolutional autoencoders and spectral clustering to provide pseudo labels for image data. This data can be used as weakly-labeled data for training and evaluating classifiers which require supervision. The primary weaknesses of previous works lies in their inability to isolate the object of interest in an image and cluster similar images together. We address these issues by denoising input images to remove pixels which do not contain data pertinent to the target. Additionally, we introduce a voting method for label selection to improve the clustering results. Our extensive experimentation on several benchmark datasets demonstrates that the proposed CSC method achieves competitive performance with state-of-the-art methods.more » « less