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FOptM-share TTNPE Oct 16, 2019
OtherTest TTNPE Oct 16, 2019
Self_Tool TTNPE Oct 16, 2019
TNPE TTNPE Oct 16, 2019
TT_Approximate TTNPE Oct 16, 2019
TT_TensNet TTNPE Oct 16, 2019
mdmp TTNPE Oct 16, 2019
tensorlab TTNPE Oct 16, 2019
README.md TTNPE Oct 16, 2019
Readme.rtf TTNPE Oct 16, 2019
demo.m TTNPE Oct 16, 2019

README.md

TTNPE

This is the demo code for paper "Tensor Train Neighborhood Preserving Embedding"

Copyright @ Wenqi Wang, 2018

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.

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