Abstract: A system and method for reconstructing high-dimensional input data points from known output data points using an Artificial Neural Network (ANN) is provided. The system includes a memory to store an ANN trained to map high-dimensional input data points to lower-dimensional output data points by defining hyperplanes and establishing layer-specific transformation matrices. An input module receives known output data points for classification and identification results. The processor applies a reverse mapping process to these output data points using pseudo-inverse matrices derived from the transformation matrices, facilitating the reconstruction of high-dimensional input data points. The reconstruction engine computes intermediate data representations by applying the pseudo-inverse matrices in reverse order, reconstructing the high-dimensional input data points.

Abstract: A system and method for generating a non-iterative Artificial Neural Network (ANN) are disclosed. The invention involves obtaining high-dimensional data points within a first high-dimensional space, which are standardized and normalized. Thereafter, a processor performs dimensionality reduction algorithm by determining successive sets of hyperplanes starting from the first high-dimensional space to progressively enhance segregation and isolation of the plurality of data points. The processor further iteratively maps each of the normalized data points from the first high-dimensional space through successive mappings across one or more intermediary low-dimensional spaces before reaching a final low-dimensional space for classification. Finally, the processor generates the ANN by establishing processing nodes that correspond to dimensions across the first high-dimensional space and intermediary low-dimensional spaces.

Abstract: The present invention provides a system and method for identifying a pattern as belonging to one of a set of predetermined classes of patterns. The system comprises a plurality of classifier blocks wherein each classifier block corresponds to a distinct predetermined class of patterns and produces a mirror image of an input pattern if the input pattern belongs to the predetermined class. The system also comprises a plurality of sub-classifier blocks wherein each sub-classifier block corresponds to a distinct predetermined sub-class of a predetermined class of patterns and is coupled to a classifier block corresponding thereto for producing a mirror image of an input pattern if the pattern belongs to the predetermined sub-class. The system further comprises an input unit for capturing the pattern for identification and an output unit for displaying at least one of a mirror image of an input pattern and an identified class and sub-class of the input pattern.

Abstract: The present invention relates to a system and method for identifying of a pattern by comparing the pattern with two or more identified patterns. The system comprises an input unit for capturing a pattern for identification, a processing unit for determining eigenfaces corresponding to the captured pattern and the two or more identified patterns, and determining orientation vectors corresponding to each determined eigenface, and a comparison unit for comparing the determined orientation vector corresponding to the captured pattern with each of the determined orientation vectors corresponding to the identified patterns.