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Feature engineering plays an important role in the success of a machinelearning model. Most of the effort in training a model goes into datapreparation and choosing the right representation. In this paper, we propose arobust feature engineering method, Randomized Union of Locally Linear Subspaces(RULLS). We generate sparse, non-negative, and rotation invariant features inan unsupervised fashion. RULLS aggregates features from a random union ofsubspaces by describing each point using globally chosen landmarks. Theselandmarks serve as anchor points for choosing subspaces. Our method provides away to select features that are relevant in the neighborhood around thesechosen landmarks. Distances from each data point to $k$ closest landmarks areencoded in the feature matrix. The final feature representation is a union offeatures from all chosen subspaces.  The effectiveness of our algorithm is shown on various real-world datasetsfor tasks such as clustering and classification of raw data and in the presenceof noise. We compare our method with existing feature generation methods.Results show a high performance of our method on both classification andclustering tasks.

RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering