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Fig. 9 Effect of the dataset dimension on the extension of the hazard maps.
PCA reduces the dataset dimension and enables visualization of individuals and populations in 3D or 4D graphics.
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Thus, for instance, when the number of projections is P = 1440 (first set of bars in Fig. 4a), the dataset dimensions are 1440 (times) 1 (times) 2048.
For the BRCA dataset, dimension r should be chosen as 2, because there was a turning point at 2 on the curve of the "explained variance" (Fig. 3a).
The diagonal gives the original dataset dimensions (array mutants × query mutants).
For all aforementioned considerations, and given the growing importance of alternative statistical approaches, we propose a new approach to reduce a dataset dimension, especially for classification purposes.
We believe that the result is caused by (1) the computation strategy of Proclus which aims for large clusters, and (2) the dataset contains dimensions in which many patients are similar to each other independent of their class label.
This resulted in dataset dimensions of 462 aligned amino acid positions from 72 sequences for EFL, and 446 positions from 82 sequences for eEF1A.
The dataset contains 61 dimensions describing clinical parameters such as sex, age, anthropometric measures, and hematological and biochemical tests.
The dataset scale and eigenvector dimension are as shown in Table 4.
In the context of high-dimensional data, two further important preparation steps often performed using the whole dataset are dimension reduction procedures such as Principal Component Analysis (PCA) and normalization for example normalization using the RMA ("Robust Multi-array Average") method [ 12] for microarray gene expression data.
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