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Property condition assessments (PCAs) (also known as the property condition report, or PCR) are due diligence projects associated with commercial real estate.Commercial property and building inspections are important for clients seeking to know the condition of a property or real estate they may be purchasing, leasing, financing or simply maintaining.
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.
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 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 .
An example of a résumé with a common format with the name John Doe. A résumé or resume (or alternatively resumé), [a] [1] is a document created and used by a person to present their background, skills, and accomplishments. Résumés can be used for a variety of reasons, but most often are used to secure new jobs, whether in the same ...
In computer engineering, a physical configuration audit (PCA) is the formal examination of the "as-built" configuration of a configuration item (CI) against its technical documentation to establish or verify the CI's product baseline. The PCA is used to examine the actual configuration of the CI that is representative of the product ...
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.
PCA may refer to: Medicine and biology. Patient-controlled analgesia; Plate count agar in microbiology; Polymerase cycling assembly, for large DNA oligonucleotides;
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