Motion capture data from human subjects exhibits considerable redundancy. In this paper, we propose novel methods for exploiting this redundancy. In particular, we set out to find a subset of motion-capture markers that are able to provide fast and high-quality predictions of the remaining markers. We then develop a model that uses this reduced marker set to predict the others. We demonstrate that this subset of original markers is sufficient to capture subtle variations in human motion. We take a data-driven modeling approach to learn piecewise local linear models from a marker-based training set. We first divide motion sequences into segments of low dimensionality. We then retrieve a feature vector from each of the motion segments and use these feature vectors as modeling primitives to cluster the segments into a hierarchy of local linear models via a divisive clustering method. The selection of an appropriate linear model for reconstruction of a full-body pose is determined automatically via a classifier driven by a reduced marker set. After offline training, our method can quickly reconstruct full-body human motion using a reduced marker set without storing and searching the large database. We also demonstrate our method's ability to generalize over a variety of motions from multiple subjects.