Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey

• URL: http://arxiv.org/abs/2106.02154v1
• Date: Thu, 3 Jun 2021 22:10:40 GMT
• Title: Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey
• Authors: Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley
• Abstract summary: We first introduce adjacency matrix, definition of Laplacian matrix, and the interpretation of Laplacian. Different optimization variants of Laplacian eigenmap and its out-of-sample extension are explained. Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap. Finally, diffusion map is introduced which is a method based on Laplacian of data and random walks on the data graph.
• Score: 5.967999555890417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This is a tutorial and survey paper for nonlinear dimensionality and feature extraction methods which are based on the Laplacian of graph of data. We first introduce adjacency matrix, definition of Laplacian matrix, and the interpretation of Laplacian. Then, we cover the cuts of graph and spectral clustering which applies clustering in a subspace of data. Different optimization variants of Laplacian eigenmap and its out-of-sample extension are explained. Thereafter, we introduce the locality preserving projection and its kernel variant as linear special cases of Laplacian eigenmap. Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection. Finally, diffusion map is introduced which is a method based on Laplacian of data and random walks on the data graph.

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