Visualizing high-dimensional data is an essential task in Data Science and\nMachine Learning. The Centroid-Encoder (CE) method is similar to the\nautoencoder but incorporates label information to keep objects of a class close\ntogether in the reduced visualization space. CE exploits nonlinearity and\nlabels to encode high variance in low dimensions while capturing the global\nstructure of the data. We present a detailed analysis of the method using a\nwide variety of data sets and compare it with other supervised dimension\nreduction techniques, including NCA, nonlinear NCA, t-distributed NCA,\nt-distributed MCML, supervised UMAP, supervised PCA, Colored Maximum Variance\nUnfolding, supervised Isomap, Parametric Embedding, supervised Neighbor\nRetrieval Visualizer, and Multiple Relational Embedding. We empirically show\nthat centroid-encoder outperforms most of these techniques. We also show that\nwhen the data variance is spread across multiple modalities, centroid-encoder\nextracts a significant amount of information from the data in low dimensional\nspace. This key feature establishes its value to use it as a tool for data\nvisualization.\n