Distance Estimation for Incomplete Data by Extreme Learning Machine

Data with missing values are very common in practice, yet many machine learning models are not designed to handle incomplete data. As most machine learning approaches can be formulated in terms of distance between samples, estimating these distances on data with missing values provides an effective way to use such models. This paper present a procedure to estimate the distances using the Extreme Learning Machine. Experimental comparison shows that the proposed approach achieves competitive accuracy with other methods on standard benchmark datasets.

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