{ "item_title" : "Multi-Label Dimensionality Reduction", "item_author" : [" Liang Sun", "Shuiwang Ji", "Jieping Ye "], "item_description" : "Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications.Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions, including:How to fully exploit label correlations for effective dimensionality reductionHow to scale dimensionality reduction algorithms to large-scale problemsHow to effectively combine dimensionality reduction with classificationHow to derive sparse dimensionality reduction algorithms to enhance model interpretabilityHow to perform multi-label dimensionality reduction effectively in practical applicationsThe authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of Drosophila gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems. A supplementary website provides a MATLAB(R) package for implementing popular dimensionality reduction algorithms.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/43/980/615/1439806152_b.jpg", "price_data" : { "retail_price" : "160.00", "online_price" : "160.00", "our_price" : "160.00", "club_price" : "160.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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