Abstract : Feature models have become one of the most widely used formalism for representing the variability among the products of a product line. The design of a feature model from a set of existing products can help stakeholders communicate on the commonal-ities and differences between the products, facilitate the adoption of mass customization strategies, or support the definition of the solution space of a product configurator (i.e. the sets of products that will be and will not be offered to the targeted customers). As the manual construction of feature models proves to be a time-consuming and error prone task, researchers have proposed various approaches for automatically deriving feature models from available product data. Existing reverse engineering techniques mostly rely on data mining algorithms that search for frequently occurring patterns between the features of the available product configurations. However, when the number of features is too large, the sparsity among the configurations can reduce the quality of the extracted model. In this paper, we discuss motivations for the development of dimensionality reduction techniques for product lines in order to support the extraction of feature models in the case of high-dimensional product spaces. We use a real world dataset to illustrate the problems arising with high dimensionality and present four research questions to address them.