Search results
Results from the WOW.Com Content Network
In a hospital setting, an intravenous PCA (IV PCA) refers to an electronically controlled infusion pump that delivers an amount of analgesic when the patient presses a button. [4] IV PCA can be used for both acute and chronic pain patients. It is commonly used for post-operative pain management, and for end-stage cancer patients. [5]
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.
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 ...
Patient-controlled analgesia; Plate count agar in microbiology; Polymerase cycling assembly, for large DNA oligonucleotides; Posterior cerebral artery; Posterior cortical atrophy, a form of dementia
HRHIS is a human resource for health information system for management of human resources for health developed by University of Dar es Salaam college of information and communication technology, Department of Computer Science and Engineering, for Ministry of Health and Social Welfare (Tanzania) and funded by the Japan International Cooperation ...
The 2014 guaranteed algorithm for the robust PCA problem (with the input matrix being = +) is an alternating minimization type algorithm. [12] The computational complexity is () where the input is the superposition of a low-rank (of rank ) and a sparse matrix of dimension and is the desired accuracy of the recovered solution, i.e., ‖ ^ ‖ where is the true low-rank component and ^ is the ...
Output after kernel PCA, with a Gaussian kernel. Note in particular that the first principal component is enough to distinguish the three different groups, which is impossible using only linear PCA, because linear PCA operates only in the given (in this case two-dimensional) space, in which these concentric point clouds are not linearly separable.
In order to build the classification models, the samples belonging to each class need to be analysed using principal component analysis (PCA); only the significant components are retained. For a given class, the resulting model then describes either a line (for one Principal Component or PC), plane (for two PCs) or hyper-plane (for more than ...