Supervised latent Dirichlet allocation for classification

(C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei. Written by Chong Wang, chongw@cs.princeton.edu, part of code is from lda-c.

This is a C++ implementation of supervised latent Dirichlet allocation (sLDA) for classification. Note that the code here is slightly different from what was described in [2] in order to speed up. Note that this is only the sLDA. The annotation part is not yet posted.

Sample data

A preprocessed 8-class image dataset [2] from Labelme. See images.tgz.

UIUC Sports annotation files: annotations and meta information. The source image files can be found here. (Note: there might be some discrepancies and it is unclear why...)

References

[1] Chong Wang, David M. Blei and Li Fei-Fei. Simultaneous image classification and annotation. In CVPR, 2009. [PDF]

[2] David M. Blei and Jon McAuliffe. Supervised topic models. In NIPS, 2007. [PDF]

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README

Note that this code requires the Gnu Scientific Library, http://www.gnu.org/software/gsl/

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TABLE OF CONTENTS

A. COMPILING

B. ESTIMATION

C. INFERENCE

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A. COMPILING

Type "make" in a shell. Make sure the GSL is installed.

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B. ESTIMATION

Estimate the model by executing:

 slda [est] [data] [label] [settings] [alpha] [k] [seeded/random/model_path] [directory]

The saved models are in two files:

 <iteration>.model is the model saved in the binary format, which is easy and
 fast to use for inference.

 <iteration>.model.txt is the model saved in the text format, which is
 convenient for printing topics or analysis using python.

The variational posterior Dirichlets are in:

 <iteration>.gamma

Data format

(1) [data] is a file where each line is of the form:

 [M] [term_1]:[count] [term_2]:[count] ...  [term_N]:[count]

where [M] is the number of unique terms in the document, and the [count] associated with each term is how many times that term appeared in the document.

(2) [label] is a file where each line is the corresponding label for [data]. The labels must be 0, 1, ..., C-1, if we have C classes.

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C. INFERENCE

To perform inference on a different set of data (in the same format as for estimation), execute:

 slda [inf] [data] [label] [settings] [model] [directory]

where [model] is the binary file from the estimation.

The predictive labels are in:

 inf-labels.dat

The variational posterior Dirichlets are in:

 inf-gamma.dat