Dimensionality reduction of multidimensional temporal data through regression ?
A new method for pattern classification of multidimensional temporal data/images is proposed. In temporal data/images, each feature of a sample/pixel is not just a single numerical value, but a set (vector) of real values. The method proposed transforms the pattern of change in the feature values, over time, into representative patterns, termed as symbolic objects [Bock, Diday (Eds.), Analysis of Symbolic Data, Springer Verlag, 2000], which are obtained through regression lines. Since a regression line symbolizes a sequence of numerical values of a feature vector, the so defined symbolic object accomplishes dimensionality reduction of the temporal data. A new distance measure is devised to measure the distances between the symbolic objects (fitted regression lines) and clustering is performed. The method is very versatile and is readily applicable to any multidimensional temporal image. The algorithm is tested on two different data sets.
dimensionality reduction regression pattern classification symbolic data clustering temporal feature analysis data assimilation