Fast Spectral Clustering Using Autoencoders and Landmarks
This work addresses the computational bottleneck in spectral clustering for researchers and practitioners dealing with large datasets, though it appears incremental as it builds on existing methods with specific optimizations.
The paper tackles the high computational complexity of spectral clustering by introducing an algorithm that uses landmarks and a deep autoencoder to perform eigen decomposition efficiently, achieving a complexity of O(np) where n is the number of data points and p is the number of landmarks.
In this paper, we introduce an algorithm for performing spectral clustering efficiently. Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition. In this work, we first build the adjacency matrix of the corresponding graph of the dataset. To build this matrix, we only consider a limited number of points, called landmarks, and compute the similarity of all data points with the landmarks. Then, we present a definition of the Laplacian matrix of the graph that enable us to perform eigen decomposition efficiently, using a deep autoencoder. The overall complexity of the algorithm for eigen decomposition is $O(np)$, where $n$ is the number of data points and $p$ is the number of landmarks. At last, we evaluate the performance of the algorithm in different experiments.