Abstract: Tensor robust principal component analysis (TRPCA), as a popular linear low-rank method, has been widely applied to various visual tasks. The mathematical process of the low-rank prior is ...
In microbiome studies, addressing the unique characteristics of sequence data—such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality—is crucial for accurate ...
Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...
Abstract: This paper addresses the tensor robust principal component analysis (RPCA) by employing linear slim transforms along the mode-3 of the tensor. Previous works have empirically shown the ...
The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a compelling case with benchmark data that standard techniques perform poorly. The ...
ABSTRACT: Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results