Multivariate Classification Techniques Research Articles

Feature enhancement techniques for the detection of spinal cord injury

Evoked potentials ( EPs) produced by median nerve stimulation and recorded from the somatosensory receiving area of cat cortex were obtained from anaesthetized cats and averaged before and after one of two treatments. Seven data transformations were applied to the average EPs with the idea of emphasizing features of the EP pertinent for classification. The transformations applied involved both time and frequency domain representation of the original average EPs. Statistical multivariate classification techniques were then used to obtain canonical plots of the original and transformed EPs. In every case, frequency domain analysis resulted in better separation between treatment categories. It was also found that the power spectrum which was normalized by its average power proved the best for classification purposes.

SUCCESSFUL INDUSTRIAL INNOVATIONS: A MULTIVARIATE ANALYSIS*

The complexity of the process through which new technology is developed requires the use of multivariate models and research hypotheses. However, few studies of innovation have attempted these tools; because of the expense of obtaining large enough samples and the qualitative or nominal nature of most data on technical change.Descriptive data are available for one large sample (n = 567) of new products and processes. Five industries are represented. These data include characteristics of the innovations such as cost and change required in the production process, and characteristics of the basic information used in their development, such as its source and initiation.While there are severe limitations and problems of reliability and validity for the data, some encouraging results have been obtained using a multivariate binary classification technique to test several hypotheses. Industry differences have been predicted on the basis of various characteristics of their successful innovations (R2= 0.27) and on the basis of communication characteristics (R2= 0.17). Some more rigorous approaches are suggested for future work.