Representative Selection with Structured Sparsity

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  • 構造正則化学習に基づく代表事例選択
  • コウゾウ セイソクカ ガクシュウ ニ モトズク ダイヒョウ ジレイ センタク

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Abstract

<p>We propose a novel formulation to find representatives based on structured sparse learning. To optimize our objective function, we propose the fast iterative shrinkage-thresholding algorithm combined with the proximal-Dykstra method and the calculation of parametric maximum ows. Experiments on three real-world image datasets validate the effectiveness of the proposed method in finding exemplars with diversity and representativeness.</p>

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