Supervised Dimensionality Reduction and Visualization using Centroid-encoder

• URL: http://arxiv.org/abs/2002.11934v2
• Date: Fri, 28 Feb 2020 23:22:24 GMT
• Title: Supervised Dimensionality Reduction and Visualization using Centroid-encoder
• Authors: Tomojit Ghosh and Michael Kirby
• Abstract summary: Centroid-Encoder (CE) method is similar to autoencoder but incorporates label information to keep objects of a class close together in reduced visualization space. CE exploits nonlinearity and labels to encode high variance in low dimensions while capturing the global structure of the data. We show that when the data variance is spread across multiple modalities, centroid-encoder extracts a significant amount of information from the data in low dimensional space.
• Score: 1.2487990897680423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visualizing high-dimensional data is an essential task in Data Science and Machine Learning. The Centroid-Encoder (CE) method is similar to the autoencoder but incorporates label information to keep objects of a class close together in the reduced visualization space. CE exploits nonlinearity and labels to encode high variance in low dimensions while capturing the global structure of the data. We present a detailed analysis of the method using a wide variety of data sets and compare it with other supervised dimension reduction techniques, including NCA, nonlinear NCA, t-distributed NCA, t-distributed MCML, supervised UMAP, supervised PCA, Colored Maximum Variance Unfolding, supervised Isomap, Parametric Embedding, supervised Neighbor Retrieval Visualizer, and Multiple Relational Embedding. We empirically show that centroid-encoder outperforms most of these techniques. We also show that when the data variance is spread across multiple modalities, centroid-encoder extracts a significant amount of information from the data in low dimensional space. This key feature establishes its value to use it as a tool for data visualization.

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