用神经网络直接学习谱聚类的聚类归属，提升可扩展性和泛化能力。
Neural Normalized Cut: A Differential and Generalizable Approach for Spectral Clustering
- 用神经网络重参数化聚类归属，替代传统特征分解。
- 在基准数据集上优于传统方法，且支持新样本快速聚类。
- 适合大规模数据聚类，无需重复计算相似度矩阵。
谱聚类作为常用的数据聚类工具，需对给定的相似度矩阵进行特征分解以获得谱嵌入，但该过程缺乏泛化能力和可扩展性。此外，所得谱嵌入难以准确逼近真实划分，通常还需通过k-means进行量化。本文提出一种简单而有效的可扩展、可泛化的新型方法——神经归一化割（NeuNcut），直接学习谱聚类的聚类归属。NeuNcut通过神经网络重新参数化未知的聚类归属，并利用松弛后的归一化割损失函数，通过随机梯度下降进行训练。结果表明，该方法具备良好的泛化能力，可直接推断未见样本的聚类归属，从而高效处理超大规模聚类任务。我们在合成数据和多个基准数据集上进行了大量实验，验证了方法的有效性与优越性。代码已开源：https://github.com/hewei98/NeuNcut。
原文摘要 · Abstract (English)
Spectral clustering, as a popular tool for data clustering, requires an eigen-decomposition step on a given affinity to obtain the spectral embedding. Nevertheless, such a step suffers from the lack of generalizability and scalability. Moreover, the obtained spectral embeddings can hardly provide a good approximation to the ground-truth partition and thus a k-means step is adopted to quantize the embedding. In this paper, we propose a simple yet effective scalable and generalizable approach, called Neural Normalized Cut (NeuNcut), to learn the clustering membership for spectral clustering directly. In NeuNcut, we properly reparameterize the unknown cluster membership via a neural network, and train the neural network via stochastic gradient descent with a properly relaxed normalized cut loss. As a result, our NeuNcut enjoys a desired generalization ability to directly infer clustering membership for out-of-sample unseen data and hence brings us an efficient way to handle clustering task with ultra large-scale data. We conduct extensive experiments on both synthetic data and benchmark datasets and experimental results validate the effectiveness and the superiority of our approach. Our code is available at: https://github.com/hewei98/NeuNcut.
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