2017/02/16 by Akshay Soni, Yashar Mehdad, Soni, Akshay +1 · 2 citations
Computer Science · Mathematics · #Artificial intelligence #Computer science #Epistemology #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Isometry (Riemannian geometry) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Mathematics #Natural Language Processing Techniques #Natural language processing #Philosophy #Property (philosophy) #Pure mathematics #Text and Document Classification Technologies #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1702.05181
published in arXiv (Cornell University) (Cornell University) · 6 pages
arxiv created 2017/02/16 · openalex publication_date 2017/02/16 · arxiv updated 2017/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that only a few labels are active in any given datapoint as compared to the total number of labels. However, only a small number of existing work take direct advantage of this inherent extreme sparsity in the label space. By the virtue of Restricted Isometry Property (RIP), satisfied by many random ensembles, we propose a novel procedure for multilabel learning known as RIPML. During the training phase, in RIPML, labels are projected onto a random low-dimensional subspace followed by solving a least-square problem in this subspace. Inference is done by a k-nearest neighbor (kNN) based approach. We demonstrate the effectiveness of RIPML by conducting extensive simulations and comparing results with the state-of-the-art linear dimensionality reduction based approaches.