文件列表:

calc_w.m (1339, 2018-01-15)
data\balance.mat (2318, 2018-01-15)
data\breast.mat (3431, 2018-01-15)
data\german.mat (10867, 2018-01-15)
data\glass.mat (6047, 2018-01-15)
data\heart.mat (7590, 2018-01-15)
data\ionosphere.mat (55109, 2018-01-15)
data\iris.mat (1384, 2018-01-15)
data\liver.mat (4180, 2018-01-15)
data\musk.mat (1315650, 2018-01-15)
data\parkinsons.mat (31332, 2018-01-15)
data\pima.mat (24916, 2018-01-15)
data\ringnorm.mat (1152965, 2018-01-15)
data\segment.mat (179753, 2018-01-15)
data\sonar.mat (54798, 2018-01-15)
data\twonorm.mat (1151109, 2018-01-15)
data\vote.mat (2424, 2018-01-15)
data\waveform3.mat (270846, 2018-01-15)
data\wine.mat (6557, 2018-01-15)
data\wpbc.mat (46992, 2018-01-15)
data\zoo.mat (749, 2018-01-15)
distance_matrix.m (266, 2018-01-15)
FSVM_Cg_ForClass.m (3665, 2018-01-15)
FSVM_C_ForClass.m (2202, 2018-01-15)
FSVM_rho_ForClass.m (1161, 2018-01-15)
FSVM_train_St.m (1910, 2018-01-15)
FSVM_train_Sw.m (1832, 2018-01-15)
FSVM_train_update_St.m (2028, 2018-01-15)
FSVM_train_update_Sw.m (1947, 2018-01-15)
init_params.m (841, 2018-01-15)
kernel.m (555, 2018-01-15)
kernel_NewData.m (598, 2018-01-15)
kernel_PCA.m (1221, 2018-01-15)
kernel_PCA_NewData.m (543, 2018-01-15)
libsvm-3.22\COPYRIGHT (1497, 2018-01-15)
libsvm-3.22\FAQ.html (83089, 2018-01-15)
libsvm-3.22\heart_scale (27670, 2018-01-15)
libsvm-3.22\java\libsvm\svm.java (64242, 2018-01-15)
libsvm-3.22\java\libsvm\svm.m4 (63439, 2018-01-15)
libsvm-3.22\java\libsvm\svm_model.java (868, 2018-01-15)
... ...

# FSVM ________ ## Matlab implementations of the linear and kernel FSVM algorithm. ________ ## Datasets 1. The codes take the "breast" as an example. 2. Modify the setname variable in UCI_Linear_FSVM.m and UCI_Kernel_FSVM.m to evaluate other dataset. (UCI datasets: https://archive.ics.uci.edu/ml/datasets.html) 3. The data should be first split into the training and test data randomly (you can set more split than 10). (refer to "breast" dataset) 4. Please note the pre-process of the original data. Some of the UCI dataets have been pre-processed while some are not. Please pre-process the original data if necessary, which is important. __________ ## Description and Instructions ### Note: In the released codes, for better understanding the algorithm, four variants are provided to evaluate the utility of the total scatter matrix and the intra-class scatter matrix, with and withour updating the scatter matrix during the alternative updating stage. * FSVM_train_St.m - GBCD algorithm to solve FSVM by using the total scatter matrix without updating the scatter matrix * FSVM_train_update_St.m - GBCD algorithm to solve FSVM by using the total scatter matrix with updating the scatter matrix * FSVM_train_Sw.m - GBCD algorithm to solve FSVM by using the intra-class scatter matrix without updating the scatter matrix * FSVM_train_update_Sw.m - GBCD algorithm to solve FSVM by using the intra-class scatter matrix with updating the scatter matrix. * calc_w.m - the calculation of w from LibSVM Toolbox * update_St.m, update_Sw.m - update the total scatter matrix (St) and the intra-class scatter matrix (Sw) with the reweighted scatter matrix benefitted from the softmax function * kernel_PCA.m, kernel.m, kernel_PCA_NewData, kernel_NewData, distance_matrix.m - Kernel PCA. * swb.m - the within-class scatter matrix (Sw) and between-class scatter matrix (Sb) * init_params.m - settings of the default parameters * FSVM_C_ForClass.m, FSVM_Cg_ForClass.m, FSVM_rho_ForClass.m - Fine tune for optimal hyper-parameters ### Quickstart 1. Put the dataset in path ./data 2. Download the LibSVM Toolbox from http://www.csie.ntu.edu.tw/~cjlin/cgi-bin/libsvm.cgi?+http://www.csie.ntu.edu.tw/~cjlin/libsvm+zip, complie it and addpath to the matlab workspace 3. For the linear FSVM, run UCI_Linear_FSVM.m 4. For the kernel FSVM, run UCI_Kernel_FSVM.m 5. To evaluate differet variants, please note to change the GBCD function in corresponding place (with annotation in the codes) ________ ## References [1] X. Wu, W. Zuo*, L. Lin, W. Jia and D. Zhang."F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation", IEEE TNNLS 2018. [2] H. Do, A. Kalousis, and M. Hilario, "Feature weighting using margin and radius based error bound optimization in svms", in Proc. ECML PKDD, 2009. [3] H. Do and A. Kalousis, "Convex formulations of radius-margin based support vector machines", in Proc. ICML, 2013. [4] P. K. Shivaswamy and T. Jebara, "Maximum relative margin and datadependent regularization", JMLR, 2010. [5] C.-C Chang, and C.-J Lin. "LIBSVM : a library for support vector machines", ACM TIST, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm ________ ## Citation If you find the code and dataset useful in your research, please consider citing: @article{wu2018fsvm, title={F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation}, author={Wu, Xiaohe and Zuo*, Wangmeng and Lin, Liang and jia, Wei and Zhang, David}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2018}} ________ ## Contents Feedbacks and comments are welcome! Feel free to contact us via [xhwu.cpsl.hit@gmail.com] or [angela612@126.com]. ________ ## Liscense Copyright (c) 2018, Xiaohe Wu All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution

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