This package is the source files accompanying the following paper:
Y. Ding and B. Li, "VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection", 
full manuscript is available here: https://arxiv.org/abs/2001.05578

The package is free for any researcher to use, as long as they clearly cite the above source, 
do not redistrbute, and do not use for commercial purchase. Thank you!

 
Usage:

main.m file shows a demo of the proposed approach, the two view version.
Note all code is written using Matlab R2019a, 
a different version of Matlab may lead to a slightly different result.

Dataset:

We provide a synthetic dataset of both clean and noise version in the dataset folder.
Training set consists of two files: p_train_textual.txt and p_train_visual.txt 
and test set consists of p_test_textual.txt and p_test_visual.txt.

Each line contains the textual/visual word Index for a document. 
Note word Index. Note the index are integers ranging from 0 to vocabulary_size-1.

In the clean version, the visual vocabulary size is 800 and textual vocabulary size is 80. 
In the noise version, the visual vocabulary size is 1600 and textual vocabulary size is 160.

Output:

Model parameters and filtered visual/textual words Index.

Evaluation:

err_rate and the rank of the groundtruth image Index in the retrieved list.