Exact(19)
Principal component analysis (PCA) is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.
Smith (2002) comments that PCA is a way of identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences.
Significant changes in moisture content occur in the snus production process, and expressing the data on a dry-weight basis to allow for these changes showed more significant increases from 182 ng/g to 1202 ng/g DWB.
Each user j is associated with a generic monotonic strictly concave utility function U j (γc,j) of the achieved SINR, expressing the data rates attainable on the wireless links.
Expressing the data as the fold difference between double-positive cells versus Myog single positives clearly illustrates the effects of SUV420H1_i2 and SUV420H2.
By expressing the data in log values, changes in observations of fixed proportion, either up or down, become linear, consistent with a constant fraction for physiologically meaningful changes in parameter values [17].
Similar(41)
We describe here, the different microarray technologies, their use and the different ways to express the data as genes expression patterns.
The measured values later analyzed using different temperature dependent exponential expressions and found that the Mott variable range hopping conduction model was successful to express the data.
We write the deformed curve as (overline {gamma }), and we also express the data associated with (overline {gamma }) by putting —.
PCA expresses the data as a linear combination of the most significant features of a given posture.
PCA can be used to identify the patterns from the data, and to express the data in a way that highlights their similarities and differences.
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