t-Distributed stochastic neighbor embedding spectral clustering

• Published in: 2017 International Joint Conference on Neural Networks (IJCNN) pp. 1628 - 1632
• Main Authors: Rogovschi, Nicoleta, Kitazono, Jun, Grozavu, Nistor, Omori, Toshiaki, Ozawa, Seiichi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2017
• Subjects: Clustering algorithms; Clustering methods; Data mining; Data visualization; Laplace equations; Stochastic processes; Unsupervised learning
• DOI: 10.1109/IJCNN.2017.7966046

This paper introduces a new topological clustering approach to cluster high dimensional datasets based on t-SNE (Stochastic Neighbor Embedding) dimensionality reduction method and spectral clustering. Spectral clustering method needs to construct an adjacency matrix and calculate the eigen-decomposition of the corresponding Laplacian matrix [1] which are computational expensive and is not easy to apply on large-scale data sets. One of the issue of this problem is to reduce the dimensionality before to cluster the dataset. The t-SNE method which performs good results for visualization allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. Using t-SNE during the learning process will allow to reduce the dimensionality and to preserve the topology of the dataset by increasing the clustering accuracy. We illustrate the power of this method with several real datasets. The results show a good quality of clustering results and a higher speed.