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Plate count agar (PCA), also called standard methods agar (SMA), is a microbiological growth medium commonly used to assess or to monitor "total" or viable bacterial growth of a sample. PCA is not a selective medium. The total number of living aerobic bacteria can be determined using a plate count agar which is a substrate for bacteria to grow on.
Within the field of molecular biology, a protein-fragment complementation assay, or PCA, is a method for the identification and quantification of protein–protein interactions. In the PCA, the proteins of interest ("bait" and "prey") are each covalently linked to fragments of a third protein (e.g. DHFR, which acts as a "reporter").
It is also used to prevent the formation of slime in industrial waters. Residual amounts of the compound and its metabolites can be found in crops. The degradation products, PCA and PCTA have been found in farming soils and in river sediments.
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.
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 ...
PCA may refer to: Medicine and biology. Patient-controlled analgesia; Plate count agar in microbiology; Polymerase cycling assembly, for large DNA oligonucleotides;
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.
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 ...