Tensor linear laplacian discrimination for feature extraction

A system for extracting discriminant features from tensor based data, the system with an input, a memory, and a processor in communication with the input and the memory, the system comprising:

• a weight generator module to receive a plurality of data samples and generate at least one sample weight based on a contextual distance for each of the plurality of data samples, and further to generate a class weight based on a contextual distance for a first class of data samples;
• a scatter module in communication with the weight generator, the scatter module to receive the at least one sample weight and the class weight and calculate a within-class scatter for a class of data samples using the at least one sample weight, and to calculate a between-class scatter for multiple classes of data samples using the class weight;
• an unfolding module coupled with the weight generator module and the scatter module, the unfolding module to perform a mode-k matrix unfolding on the within-class scatter to generate a mode-k within-class scatter matrix and to perform a mode-k matrix unfolding on the between-class scatter to generate a mode-k between-class scatter matrix;
• and
• a projection matrix module coupled with the scatter module and the unfolding module, the projection matrix module to generate at least one orthogonal projection matrix using the mode-k within-class scatter matrix and the mode-k between-class scatter matrix.

A computer-readable medium comprising instructions executable by a computing device to enable discriminant feature extraction using tensor based data, the instructions being executable to perform a method comprising:

• generating at least one sample weight based on a contextual distance for each of a plurality of data samples in a sample set;
• generating a class weight based on a contextual distance for a first class of data samples in a sample set;
• calculating a within-class scatter for a class of data samples in a sample set, the within-class scatter calculated using the at least one sample weight;
• calculating a between-class scatter for multiple classes of data samples in a sample set, the between-class scatter calculated using the class weight;
• performing a mode-k matrix unfolding on the within-class scatter to generate a mode-k within-class scatter matrix;
• performing a mode-k matrix unfolding on the between-class scatter to generate a mode-k between-class scatter matrix;
• and
• generating at least one orthogonal projection matrix using the mode-k within-class scatter matrix and the mode-k between-class scatter matrix, wherein a contribution from the mode-k between-class scatter matrix is given more weight than a contribution from the mode-k within-class scatter matrix.