Author/Authors :
Alvis Salenieks، نويسنده , , Oh Sang Woo and Michael Mavrovouniotis، نويسنده ,
Latin Abstract :
The purpose of this article is to show the effectiveness of a positive linear decomposition in the derivation
of robust features of high-dimensional dynamic measurements, in order to achieve effective pattern recognition
and classification, The method begins with the singular value decomposition, projecting a matrix
of dynamic process measurements (taken at uniform intervals over some time-window) onto a low-dimensional
subspace. A convex cone, defined by the non-negativity of measurements, is then created. For normalization
purposes a polygon, whose corners specify the feature vectors of the data, is formed by
intersecting the cone with a plane. This polygon is reduced to a triangle with only the three most representative
corners. The net effect of these steps is that the original orthogonal basis of the subspace (consisting
of the first three principal components) is replaced by a new, non-orthogonal basis, which offers
the advantage of containing only positive measurements and requiring only positive superposition of basis
vectors to span the physically meaningful portion of the subspace. One of the vectors in this basis is
selected as the feature vector for pattern recognition; a spanning tree created from the feature vectors classifies
the patterns. The feature vectors from the new basis are much more robust with respect to changes
in the width of the time window, and classification was possible even with feature vectors of differing time
windows.
NaturalLanguageKeyword :
Singular value decomposition , Pattern recognition , classification , Feature extraction
