Results 11 to 20 of about 141,001 (306)
Transferred Dimensionality Reduction [PDF]
, 2008 Dimensionality reduction is one of the widely used techniques for data analysis. However, it is often hard to get a demanded low-dimensional representation with only the unlabeled data, especially for the discriminative task. In this paper, we put forward a novel problem of Transferred Dimensionality Reduction, which is to do unsupervised ...
Zheng Wang 0010 +2 more
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Dimensionality Reduction by Weighted Connections between Neighborhoods [PDF]
Abstract and Applied Analysis, 2014 Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. This paper introduces a dimensionality reduction technique by weighted connections between neighborhoods to improve K ...
Fuding Xie, Yutao Fan, Ming Zhou
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Dimensionality reduction in Bayesian estimation algorithms [PDF]
Atmospheric Measurement Techniques, 2013 An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm.
G. W. Petty
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Evaluating dimensionality reduction for genomic prediction
Frontiers in Genetics, 2022 The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials.
Vamsi Manthena +8 more
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2D Dimensionality Reduction Methods without Loss [PDF]
Journal of Artificial Intelligence and Data Mining, 2019 In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application.
S. Ahmadkhani, P. Adibi, A. ahmadkhani
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Supervised dimensionality reduction for big data
Nature Communications, 2021 Biomedical measurements usually generate high-dimensional data where individual samples are classified in several categories. Vogelstein et al. propose a supervised dimensionality reduction method which estimates the low-dimensional data projection for ...
Joshua T. Vogelstein +6 more
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Analyzing Quality Measurements for Dimensionality Reduction
Machine Learning and Knowledge Extraction, 2023 Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance ...
Michael C. Thrun +2 more
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Haisu: Hierarchically supervised nonlinear dimensionality reduction.
PLoS Computational Biology, 2022 We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the ...
Kevin Christopher VanHorn +1 more
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Dimensionality reduction in neuroscience [PDF]
Current Biology, 2016 The nervous system extracts information from its environment and distributes and processes that information to inform and drive behaviour. In this task, the nervous system faces a type of data analysis problem, for, while a visual scene may be overflowing with information, reaching for the television remote before us requires extraction of only a ...
Rich, Pang +2 more
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Nonlinear dimensionality reduction for clustering [PDF]
Pattern Recognition, 2020 Abstract We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters.
Sotiris K. Tasoulis +2 more
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