We chose an internal coordinate system (described below) for each trajectory (similar to that used in [6] with the gromos [10] method in GROMACS [11] program for clustering) to apply dimension reduction.
Principal component analysis (PCA) is among the most commonly applied dimension reduction techniques suitable to denoise data.
Even so, discussion on applied dimension reduction technique regarding its validity from the aspect of information loss can be rarely found in the literature.
The industrial CASE students who spoke to Next Wave felt that working on a project relevant to industry added an applied dimension to their work that would otherwise have been missing.
The input of the subtractive clustering algorithm is set as. From (18), it is seen that the wider the range of values that can take, the smaller is the dimension of the clusters (the same analogy applies dimension, as well).
The second method applies dimension reduction techniques, such as principle component analysis (PCA) [12], [19] and Fourier transformation [20], to genotype data and produces a set of orthogonal predictors capturing majority of the genetic information of candidate genetic regions.
There is a rich history of applying dimension-reduction techniques, such as factor analysis and multidimensional scaling, to behavioral data to recover the underlying dimensions of meanings of words, including classic studies of color terms [4], verbs of motion [5], animals [6], and a variety of different domains [7].
Firstly, the method applies dimension reduction and smooth techniques.
LSA is a vector-based semantic similarity method that applies dimension reduction on document-term matrix before calculating cosine similarity of two terms.
Solberg et al. (2009) applied dimension reduction methods, such as the partial least squares regression (PLSR) and principal component regression (PCR) to genomic selection and showed that their performance was inferior to BayesB, although both methods were computationally faster and simpler.
