Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement

Abstract

In the last few years, large improvements in image clustering have been driven by the recent advances in deep learning. However, due to the architectural complexity of deep neural networks, there is no mathematical theory that explains the success of deep clustering techniques. In this work we introduce Projected-Scattering Spectral Clustering (PSSC), a state-of-the-art, stable, and fast algorithm for image clustering, which is also mathematically interpretable. PSSC includes a novel method to exploit the geometric structure of the scattering transform of small images. This method is inspired by the observation that, in the scattering transform domain, the subspaces formed by the eigenvectors corresponding to the few largest eigenvalues of the data matrices of individual classes are nearly shared among different classes. Therefore, projecting out those shared subspaces reduces the intra-class variability, substantially increasing the clustering performance. We call this method Projection onto Orthogonal Complement (POC). Our experiments demonstrate that PSSC obtains the best results among all shallow clustering algorithms. Moreover, it achieves comparable clustering performance to that of recent state-of-the-art clustering techniques, while reducing the execution time by more than one order of magnitude. In the spirit of reproducible research, we publish a high quality code repository along with the paper.

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Scikit-learn: Machine Learning in Python
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Understanding deep learning requires rethinking generalization
• Understanding Deep Convolutional Networks
• Adversarial Feature Learning
• Deep Scattering Spectrum
• Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
• Ultra-Scalable Spectral Clustering and Ensemble Clustering
• Scattering Networks for Hybrid Representation Learning
• The Role of Principal Angles in Subspace Classification
• Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift

Cited by in corpus (1)

• fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering