Unsupervised learning for feature projection: Extracting patterns from multidimensional building measurements

•Three conventional dimensionality reduction algorithms are compared.•PCA is unlikely to distort the original dataset at the cost of information loss.•Autoencoder provides a good balance between data compression and exploitation.•t-SNE is better in representing well-separated clusters than multi-collinearity.