Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.13352 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909847552786432 |
|---|---|
| author | Cao, Yang Yang, Sikun He, Kai Ma, Wenjun Liu, Ming Yang, Yujiu Weng, Jian |
| author_facet | Cao, Yang Yang, Sikun He, Kai Ma, Wenjun Liu, Ming Yang, Yujiu Weng, Jian |
| contents | Measuring similarity between incomplete data is a fundamental challenge in web mining, recommendation systems, and user behavior analysis. Traditional approaches either discard incomplete data or perform imputation as a preprocessing step, leading to information loss and biased similarity estimates. This paper presents the proximity kernel, a new similarity measure that directly computes similarity between incomplete data in kernel feature space without explicit imputation in the original space. The proposed method introduces data-dependent binning combined with proximity assignment to project data into a high-dimensional sparse representation that adapts to local density variations. For missing value handling, we propose a cascading fallback strategy to estimate missing feature distributions. We conduct clustering tasks on the proposed kernel representation across 12 real world incomplete datasets, demonstrating superior performance compared to existing methods while maintaining linear time complexity. All the code are available at https://anonymous.4open.science/r/proximity-kernel-2289. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kernel Representation and Similarity Measure for Incomplete Data Cao, Yang Yang, Sikun He, Kai Ma, Wenjun Liu, Ming Yang, Yujiu Weng, Jian Machine Learning Measuring similarity between incomplete data is a fundamental challenge in web mining, recommendation systems, and user behavior analysis. Traditional approaches either discard incomplete data or perform imputation as a preprocessing step, leading to information loss and biased similarity estimates. This paper presents the proximity kernel, a new similarity measure that directly computes similarity between incomplete data in kernel feature space without explicit imputation in the original space. The proposed method introduces data-dependent binning combined with proximity assignment to project data into a high-dimensional sparse representation that adapts to local density variations. For missing value handling, we propose a cascading fallback strategy to estimate missing feature distributions. We conduct clustering tasks on the proposed kernel representation across 12 real world incomplete datasets, demonstrating superior performance compared to existing methods while maintaining linear time complexity. All the code are available at https://anonymous.4open.science/r/proximity-kernel-2289. |
| title | Kernel Representation and Similarity Measure for Incomplete Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.13352 |