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Background of PCAPrincipal component analysis (PCA) is a statistical data analysis technique used to reduce the dimensionality of multivariate data. Understanding multivariate data can be difficult due to the number of dimensions. When more than three dimensions are being analyzed, it is difficult to visualize the data.
PCA is a well known method for reducing the dimensionality by extracting components which are uncorrelated with each other. Moreover, the first component explains as much of the variance as possible, while remaining uncorrelated with subsequent principal components. Hence it is the first principal component. Further principal components in turn, provide as much of the remaining variance as possible.
PCA is useful in a wide range of data analysis tasks where we seek to understand and analyse sequential or time-based data in terms of their underlying composition, where the composition signals are as independent of each other as possible.
PCA/X employs singular value decomposition to separate linearly mixed input signals in a block-based manner without user intervention.
PCA/X ActiveX and COM Control
PCA/X is an ActiveX DLL that can be used in wide range of Windows applications.
It requires no user interface and can be accessed by any ActiveX compatible development environment, including VB, ASP, VBA, VC++, Delphi, Borland C++ Builder
and various programming environments.
Matrix data used with PCA/X and returned from the control can have different index starting values. This means that you can choose to index your data from 0 or 1. PCA/X will pass the data back in an array indexed from the value you specify in a property of the control. All data used and returned with PCA/X is in double format. This means it is suitable for use with Visual Basic and Visual C++. Moreover, the data is in a format compatible with further numeric processing. Hence, if you wish to use the data with other controls that can use double format arrays, this presents no problems.