MI-HE (Multiple Instance Hybrid Estimator): multiple instance target concept learning algorithm

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NOTE: If the MI-HE Algorithm is used in any publication or presentation, the following reference must be cited: C. Jiao, C. Chen, R. McGarvey, S. Bohlman, A. Zare Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection submited to ISPRS Jounal of Photogrammetry and Remote Sensing, In Press, 2018

NOTE: If the code in this repository is used to prepare any publication or presentation, the following reference must be cited:
Changzhe Jiao, & Alina Zare. (2018, July 24). GatorSense/MIHE: Initial Release (Version 0.1). Zenodo. http://doi.org/10.5281/zenodo.1320109

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The command to run the MI-HE algorithm:

[ D, D_initial ,obj_func,obj_pos,obj_neg,obj_discrim] = MI_HE( pos_bags,neg_bags,parameters )

% Inputs:
% pos_bags - positive data, in Maltab cell format, each cell is a positive bag
% neg_bags - negative data, in Matlab cell format, each cell is a negative bag
% parameters - struct - parameter structure which can be set using the MI_HE() function
%
% Outputs:
% D - estimated concept set, d by T+M, d is the dimension of the input data, T and M account for the number of target and background concepts, respectively
% D_initial - Initialization for D
% obj_func - total objective function value
% obj_pos - objective function value for the generalized mean part
% obj_neg - objective function value for the fidelity part
% obj_discrim - objective function value for the discriminative part


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Files explanation:

'...\MIHE_Demo':
README.txt - this file
demo_MIHE_overlap_bags - repeats the synthetic experiment of MI-HE on incomplete background knowledge with SNR 20dB, corresponds to part of the results shown in Fig. 3(a) of the refered paper
incomplete_backgr_knowledge_train.mat - the synthetic data using for incomplete background knowledge experiment

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'...\MIHE_Demo\MIHE_Code':
MI_HE.m - MI-HE (Multiple Instance Hybrid Estimator): multiple instance target concept learning algorithm
HS_unmixing.m - given concepts and solve representations with or without sum to one constraint
MIHE_D_Update.m - the D update equation
MIHE_func_logJ.m - computes the objective functional value
MIHE_gradient_Jdk.m - computes the gradient of the kth non-target concept
MIHE_gradient_Jdt.m - computes the gradient of the tth target concept
MIHE_initialize.m - initializes MI-HE concepts using VCA or kmeans
MIHE_iterations.m - interatively computes the concepts D and the data representation A
MIHE_parameters.m - generates the parameters structure for MIHE
MIHE_Prob_update.m - computes the proablity of each instance from positive bags to be positive or negative bags to be negative
MIL_format_bag2inst.m - transforms MIL data from cells to matrix
MIL_format_inst2bag_no_GT.m - transforms MIL data from matrix to cells
SP_coding.m - implements the iterative soft thresholding algorithm (ISTA)
normalize.m - normalization function
VCA.m - Vertex Component Analysis algorithm, see reference of the referred paper for more details

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Latest Revision: July, 2018

% Contact: Alina Zare
% University of Florida, Department of Electrical and Computer Engineering
% Email Address: azare@ece.ufl.edu

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