研究目的
To provide an overview of methods for reducing the number of features and data instances in astronomical data, characterize and classify available data reduction algorithms, and identify solutions for present and future challenges in astronomy.
研究成果
The paper concludes that data reduction techniques are essential for handling big data in astronomy, identifies promising algorithms like Mitra et al.'s data condensation and t-SNE for future challenges, and emphasizes the need for scalable and parameter-efficient methods.
研究不足
The paper is a survey and does not conduct new experiments, so it may not address all technical constraints or optimization areas. It focuses on object-based data and excludes instrument-level data reduction.
1：Experimental Design and Method Selection:
The paper is a survey and review, not an experimental study. It summarizes existing methods and datasets without conducting new experiments.
2：Sample Selection and Data Sources:
References various astronomical datasets such as Hipparcos, SDSS, 2MASS, Gaia, and LSST, but no specific samples are selected or used in experiments.
3：List of Experimental Equipment and Materials:
No experimental equipment or materials are listed as it is a review paper.
4：Experimental Procedures and Operational Workflow:
No experimental procedures are described; instead, it reviews methodologies from literature.
5：Data Analysis Methods:
Discusses data reduction techniques like PCA, Kernel PCA, Isomap, LLE, Laplacian Eigenmaps, Diffusion Maps, feature selection, and data condensation, but no specific analysis is performed.
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