ADRS-CNet：一种用于DNA存储聚类算法的自适应降维选择与分类网络
ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms
AI总结：
本文提出ADRS-CNet，通过多层感知机对DNA序列特征分类并自适应选择降维方法，显著提升聚类效果，缓解维度灾难。
摘要：
DNA存储技术因其高存储密度、长期保存、低维护成本和紧凑体积，为解决海量数据存储提供了新的可能。为提高存储信息的可靠性，碱基错误和存储序列缺失是必须面对的挑战。目前，通过对测序序列进行聚类和比较来尽可能恢复原始序列信息。然而，将不同长度的DNA序列提取为特征会导致维度灾难，需要加以克服。为解决此问题，通常采用PCA、UMAP和t-SNE等技术将高维特征投影到低维空间。鉴于这些方法在不同数据集上的降维效果存在差异，本文提出训练一个多层感知机模型对输入DNA序列特征进行分类，并自适应选择最合适的降维方法以增强后续聚类结果。通过在开源数据集上测试并与多种基线方法比较，实验结果表明该模型展现出更优的分类性能，并显著改善了聚类效果。这表明该方法有效缓解了维度灾难对聚类模型的影响。
英文摘要：
DNA storage technology offers new possibilities for addressing massive data storage due to its high storage density, long-term preservation, low maintenance cost, and compact size. To improve the reliability of stored information, base errors and missing storage sequences are challenges that must be faced. Currently, clustering and comparison of sequenced sequences are employed to recover the original sequence information as much as possible. Nonetheless, extracting DNA sequences of different lengths as features leads to the curse of dimensionality, which needs to be overcome. To address this, techniques like PCA, UMAP, and t-SNE are commonly employed to project high-dimensional features into low-dimensional space. Considering that these methods exhibit varying effectiveness in dimensionality reduction when dealing with different datasets, this paper proposes training a multilayer perceptron model to classify input DNA sequence features and adaptively select the most suitable dimensionality reduction method to enhance subsequent clustering results. Through testing on open-source datasets and comparing our approach with various baseline methods, experimental results demonstrate that our model exhibits superior classification performance and significantly improves clustering outcomes. This displays that our approach effectively mitigates the impact of the curse of dimensionality on clustering models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
