This is the demo code for paper "Tensor Train Neighborhood Preserving Embedding"
To run the experiment Fig.3 and Fig.6 of the paper Tensor Train Neighborhood Preserving Embedding
(1) Data processing: download Weizmann Dataset from http://www.wisdom.weizmann.ac.il/~/vision/FaceBase/ and build a tensor named 'Data' with dimension 512 x 352 x 66 x 17, which represents 66 images of size 512 x 352 from 17 persons. Save it with name 'WeizmanData.mat' and put it in the folder Data_file.
(1) run demo.m for NPE algortihm comparision among KNN, TNPE and TTNPE
(2) The TTNPE-ATN is the implemented in the code Self_Tool/main_App.m. Please refer this function and the folder TT_Approximate for the detail implementation of the algorithm.
Terms of use:
The code is provided for research purpose only without any warranty. Any commercial use if prohibited
When using the code, please cite the following paper:
Tensor Train Neighborhood Preserving Embedding Wenqi Wang, Vaneet Aggarwal, and Shuchin Aeron IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2018, pp. 2724-2732
Available: https://ieeexplore.ieee.org/document/8319501/?arnumber=8319501&source=authoralert
Please contact Wenqi Wang (wang2041 [At] purdue [Dot] edu) for any questions about the code.