# Leachable Component Clustering

arXiv: https://arxiv.org/abs/2208.13217 · HTML: https://mlsanity.com/paper/2208.13217/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 28 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Twin Learning for Similarity and Clustering: A Unified Kernel Approach](https://mlsanity.com/paper/1705.00678/) (2017)
- [Optimal Clustering with Missing Values](https://mlsanity.com/paper/1902.09694/) (2019)
- [Subspace Clustering with Missing and Corrupted Data](https://mlsanity.com/paper/1707.02461/) (2017)
- [A Family of Maximum Margin Criterion for Adaptive Learning](https://mlsanity.com/paper/1810.04064/) (2018)
- [CRH: A Simple Benchmark Approach to Continuous Hashing](https://mlsanity.com/paper/1810.05730/) (2018)
- [Adaptive Matching of Kernel Means](https://mlsanity.com/paper/2011.07798/) (2020)

## Abstract (arXiv)

Clustering attempts to partition data instances into several distinctive groups, while the similarities among data belonging to the common partition can be principally reserved. Furthermore, incomplete data frequently occurs in many realworld applications, and brings perverse influence on pattern analysis. As a consequence, the specific solutions to data imputation and handling are developed to conduct the missing values of data, and independent stage of knowledge exploitation is absorbed for information understanding. In this work, a novel approach to clustering of incomplete data, termed leachable component clustering, is proposed. Rather than existing methods, the proposed method handles data imputation with Bayes alignment, and collects the lost patterns in theory. Due to the simple numeric computation of equations, the proposed method can learn optimized partitions while the calculation efficiency is held. Experiments on several artificial incomplete data sets demonstrate that, the proposed method is able to present superior performance compared with other state-of-the-art algorithms.