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      RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering

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          Abstract

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

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          Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

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            A survey on feature selection methods

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              ROBPCA: A New Approach to Robust Principal Component Analysis

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                Author and article information

                Journal
                25 April 2018
                Article
                1804.09770
                e6ff40ba-5dd7-4074-bf7d-94d3be50e276

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                9 pages
                cs.LG stat.ML

                Machine learning,Artificial intelligence
                Machine learning, Artificial intelligence

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