Target factor rotation

Factor rotation by target transformation factor analysis (TTFA)... 

Roscoe, B. A. Hopke, P. K. Comparison of Weighted and Unweighted Target Transformation Rotations in Factor Analysis, Computers and Chemistry, in press,... 

To reduce exposures, high-power X-ray tubes have been developed the limiting factor here is the heat generated at the focal spot on the target. To avoid melting, the target is rotated so that the heat is spread over an increased area (Muller, 1929 Astbury and Preston, 1934 Peiser, Rooksby, and Wilson, 1955). 

Varimax rotation is a commonly used and widely available factor rotation technique, but other methods have been proposed for interpreting factors from analytical chemistry data. We could rotate the axes in order that they align directly with factors from expected components. These axes, referred to as test vectors, would be physically significant in terms of interpretation and the rotation procedure is referred to as target transformation. Target transformation factor analysis has proved to be a valuable technique in chemo-metrics. The number of components in mixture spectra can be identified and the rotated factor loadings in terms of test data relating to standard, known spectra, can be interpreted. 

To transform the abstract factors determined in the first step into interpretable factors, rotation methods are applied. If definite target vectors can be assumed to be contained in the data, for example, a spectrum under a spectrochromatogram, the rotation of data is performed by using a target. This technique is known as target-transform factor analysis TTFA, c Example 5.6). 

The first study on curve resolution, carried out by Kaiser [1] in 1958, proposed the varimax method, wherein factor rotation was used in factor analysis. Studies by Lawton and Sylvestre of Kodak clearly picked up on curve resolution technology as a means of reaction analysis in chemistry (1971, 1974) [2]. The idea of employing rotating matrices was first used in iterative target transformation factor analysis... 

In addition to the in vitro assays described above, physical properties should be calculated for all new compounds designed for synthesis. It is necessary to keep in mind the target values for leads, such as MW < 450, clogP < 4.0, and PSA < 80. It has been demonstrated that properties such as MW and clogP increase during optimization [26], so that a lead needs to have lower values for these properties than a drug candidate. Additional factors that make up the rule-of-five [27] as well as the number of rotatable bonds as described by Veber [28] can also be tracked. While... 

Target Testing, for instance, is a procedure with the goal to rotate the abstract solution to a model factor. If this is not possible, the model is not correct. 

Iterative Target Testing is another approach. The preliminary approximations of the real factors are chosen, based on the first (VARIMAX) rotation of the abstract PCA solution. With iterative target testing the factors are transformed to the best approximations. It can be considered as LSO, where PCA and VARIMAX are supplying the model. Clusters with an arbitrary number of peaks can be deconvoluted. Six component systems are tested (Vandeginste et al. 

Another quality that is used to gauge the performance of an individual drive is seek time. This value, commonly given in milliseconds (ms), is how long it takes the actuator arm to move from rest to the position where the read/write head will access information. Additionally, because the platters rotate, once the read/write is in position, it may take a few milliseconds for the target sector to move under the read/write head. This delay is known as the latency factor. Latency values are given in milliseconds (ms). 

The abstract spectra in matrix X represent a mathematical solution to Equation 3.44 but their interpretation and identification are not clear. True spectra can be obtained by rotating the abstract spectra to best match suspected (target) real spectra. This is the process of target transformation and is a powerful technique in factor analysis since it allows real factors to be identified individually. 

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