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Dimension Reduction for High-Dimensional Data
2009With advancing of modern technologies, high-dimensional data have prevailed in computational biology. The number of variables p is very large, and in many applications, p is larger than the number of observational units n. Such high dimensionality and the unconventional small-n-large-p setting have posed new challenges to statistical analysis methods ...
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Clustering in High-dimensional Data Spaces
2002By high-dimensional we mean dimensionality of the same order as the number of objects or observations to cluster, and the latter in the range of thousands upwards. Bellman’s “curse of dimensionality” applies to many widely-used data analysis methods in high-dimensional spaces. One way to address this problem is by array permuting methods, involving row/
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Clustering High-Dimensional Data
This seminar on 'Clustering High-Dimensional Data,' led by Nikolay Oskolkov from Molecular Biosciences at Lund University, provides comprehensive training in clustering techniques using R and Python. Participants will learn to manage high-dimensional datasets, apply various clustering algorithms, and evaluate their performance to enhance their researchopenaire +1 more source
Cancer statistics in China, 2015
Ca-A Cancer Journal for Clinicians, 2016Rongshou Zheng +2 more
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