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The Pelican i1010/i1030 Micro Case series is launched; Pelican's first robot (code named "T-Rex") is installed in their Torrance, CA plant; 2008. Pelican Products purchased their largest Australian distributor and opened Pelican Australia [5] Pelican Products makes Inc. magazine's 5000 list for the second year
Multilinear principal component analysis. Multilinear principal component analysis ( MPCA) is a multilinear extension of principal component analysis (PCA) that is used to analyze M-way arrays, also informally referred to as "data tensors". M-way arrays may be modeled by linear tensor models, such as CANDECOMP/Parafac, or by multilinear tensor ...
PCPartPicker was created by Philip Carmichael in 2011. The website was substantially redesigned in February 2015. [2] [3] As of 2023 the site provides localized currencies and links to physical stores in 38 countries. The website is funded through affiliate linking to sites such as Amazon.com. [4] Additional functionality is available, such as ...
Kernel principal component analysis. In the field of multivariate statistics, kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space .
Patient-controlled analgesia ( PCA[ 1]) is any method of allowing a person in pain to administer their own pain relief. [ 2] The infusion is programmable by the prescriber. If it is programmed and functioning as intended, the machine is unlikely to deliver an overdose of medication. [ 3] Providers must always observe the first administration of ...
Principal component analysis ( PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing . The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
Sparse PCA. Sparse principal component analysis (SPCA or sparse PCA) is a technique used in statistical analysis and, in particular, in the analysis of multivariate data sets. It extends the classic method of principal component analysis (PCA) for the reduction of dimensionality of data by introducing sparsity structures to the input variables.
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