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  2. Multilinear principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Multilinear_principal...

    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 models, such as multilinear principal ...

  3. Principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Principal_component_analysis

    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.

  4. L1-norm principal component analysis - Wikipedia

    en.wikipedia.org/wiki/L1-norm_principal...

    In ()-(), L1-norm ‖ ‖ returns the sum of the absolute entries of its argument and L2-norm ‖ ‖ returns the sum of the squared entries of its argument.If one substitutes ‖ ‖ in by the Frobenius/L2-norm ‖ ‖, then the problem becomes standard PCA and it is solved by the matrix that contains the dominant singular vectors of (i.e., the singular vectors that correspond to the highest ...

  5. Patient-controlled analgesia - Wikipedia

    en.wikipedia.org/wiki/Patient-controlled_analgesia

    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 ]

  6. Shogun (toolbox) - Wikipedia

    en.wikipedia.org/wiki/Shogun_(toolbox)

    Currently Shogun supports the following algorithms: . Support vector machines; Dimensionality reduction algorithms, such as PCA, Kernel PCA, Locally Linear Embedding, Hessian Locally Linear Embedding, Local Tangent Space Alignment, Linear Local Tangent Space Alignment, Kernel Locally Linear Embedding, Kernel Local Tangent Space Alignment, Multidimensional Scaling, Isomap, Diffusion Maps ...

  7. Functional principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Functional_principal...

    Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data.Using this method, a random function is represented in the eigenbasis, which is an orthonormal basis of the Hilbert space L 2 that consists of the eigenfunctions of the autocovariance operator.

  8. Principal component regression - Wikipedia

    en.wikipedia.org/wiki/Principal_component_regression

    In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form of reduced rank regression. [1] More specifically, PCR is used for estimating the unknown regression coefficients in a standard linear regression model.

  9. Comparison of software for molecular mechanics modeling

    en.wikipedia.org/wiki/Comparison_of_software_for...

    MD MC REM QM Imp GPU Comments License Website AMBER [1] No Yes Yes Yes Yes Yes I Yes Yes High Performance MD, Comprehensive analysis tools Proprietary, Free open source: AMBER MD: Abalone: Yes Yes Yes Yes Yes Yes I Yes Yes Biomolecular simulations, protein folding. Proprietary, gratis, commercial Agile Molecule: ADF: Yes Yes Yes Yes Yes No Yes ...

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