Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
This work addresses the challenge of data separability for subspace clustering, offering a simple and effective method for real-world applications, though it is incremental as it builds on existing graph-based techniques.
The paper tackles the problem of subspace clustering by proposing a graph filtering approach to create clustering-friendly representations, achieving improved performance over state-of-the-art subspace clustering techniques and comparable results to deep learning methods in experiments on image and document datasets.
Finding a suitable data representation for a specific task has been shown to be crucial in many applications. The success of subspace clustering depends on the assumption that the data can be separated into different subspaces. However, this simple assumption does not always hold since the raw data might not be separable into subspaces. To recover the ``clustering-friendly'' representation and facilitate the subsequent clustering, we propose a graph filtering approach by which a smooth representation is achieved. Specifically, it injects graph similarity into data features by applying a low-pass filter to extract useful data representations for clustering. Extensive experiments on image and document clustering datasets demonstrate that our method improves upon state-of-the-art subspace clustering techniques. Especially, its comparable performance with deep learning methods emphasizes the effectiveness of the simple graph filtering scheme for many real-world applications. An ablation study shows that graph filtering can remove noise, preserve structure in the image, and increase the separability of classes.