Landmark multidimensional scaling (LMDS) uses a subset of data (landmark points) to solve classical MDS, where the scalability is increased but the approximation is noise-sensitive. In this paper we present an ensemble of LMDSs, referred to as landmark MDS ensemble (LMDSE), where we use a portion of the input in a piecewise manner to solve classical MDS, combining individual LMDS solutions which operate on different partitions of the input. Ground control points (GCPs) that are shared by

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... ons considered in the ensemble, allow us to align individual LMDS solutions in a common coordinate system through affine transformations. LMDSE solution is determined by averaging aligned LMDS solutions. We show that LMDSE is less noise-sensitive while maintaining the scalability as well as the speed of LMDS. Experiments on synthetic data (noisy grid) and real-world data (similar image retrieval) confirm the high performance of the proposed LMDSE.