K-means Derived Unsupervised Feature Selection using Improved ADMM
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910710001303552 |
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| author | Sun, Ziheng Ding, Chris Fan, Jicong |
| author_facet | Sun, Ziheng Ding, Chris Fan, Jicong |
| contents | Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of unsupervised feature selection is finding a subset of features such that the data points from different clusters are well separated. This paper presents a novel method called K-means Derived Unsupervised Feature Selection (K-means UFS). Unlike most existing spectral analysis based unsupervised feature selection methods, we select features using the objective of K-means. We develop an alternating direction method of multipliers (ADMM) to solve the NP-hard optimization problem of our K-means UFS model. Extensive experiments on real datasets show that our K-means UFS is more effective than the baselines in selecting features for clustering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15197 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | K-means Derived Unsupervised Feature Selection using Improved ADMM Sun, Ziheng Ding, Chris Fan, Jicong Machine Learning Artificial Intelligence Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of unsupervised feature selection is finding a subset of features such that the data points from different clusters are well separated. This paper presents a novel method called K-means Derived Unsupervised Feature Selection (K-means UFS). Unlike most existing spectral analysis based unsupervised feature selection methods, we select features using the objective of K-means. We develop an alternating direction method of multipliers (ADMM) to solve the NP-hard optimization problem of our K-means UFS model. Extensive experiments on real datasets show that our K-means UFS is more effective than the baselines in selecting features for clustering. |
| title | K-means Derived Unsupervised Feature Selection using Improved ADMM |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.15197 |