文件名称:LKDL_Package

介绍说明--下载内容均来自于网络,请自行研究使用

该程序包是一种新的算法(LKDL),该算法是基于核的字典学习,能够很好的应用于离线字典学习的预处理阶段。-The work presented in this paper describes a new approach for incorporating kernels into dictionary learning,termed Linearized Kernel Dictionary Learning (LKDL),can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively kernelizing it.
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下载文件列表





LKDL\calc_kernel.m

....\calc_support.m

....\calc_virtual_map.m

....\classify_aux.m

....\classify_aux_batch.m

....\classify_main.asv

....\classify_main.m

....\create_enlarged_MNIST.m

....\create_FIGURE_1a.m

....\create_FIGURE_1b.m

....\create_FIGURE_2.m

....\create_FIGURE_3.m

....\create_FIGURE_4a.m

....\create_FIGURE_4b.m

....\DEMO_Batch_LKDL.m

....\dictlearn_mb_simple.m

....\fkmeans.m

....\gram.m

....\init_dictionary.m

....\KKSVD.m

....\KKSVD_classify.m

....\KKSVD_train.m

....\Knorms.m

....\KOMP.m

....\KSVDCoresetAlg.m

....\KSVD_classify.m

....\KSVD_train.m

....\lambdafun.m

....\mycolormap.mat

....\normcols.m

....\randpdf.m

....\README.txt

....\RESULTS_FIGURE_1a.mat

....\RESULTS_FIGURE_1b.mat

....\RESULTS_FIGURE_2.mat

....\RESULTS_FIGURE_3.mat

....\RESULTS_FIGURE_4a.mat

....\RESULTS_FIGURE_4b.mat

....\RLS_classify.m

....\RLS_train.m

....\databases\AR.mat

....\.........\MNIST.mat

....\.........\USPS.mat

....\.........\YaleB.mat

....\Fastfood\demo.m

....\........\FastfoodForKernel.m

....\........\FastfoodPara.m

....\........\fwht_spiral.c

....\........\readme.txt

....\LCKSVD\AR.mat

....\......\classification.m

....\......\colnorms_squared_new.m

....\......\DEMO_LCKSVD_LKDL_ARface.m

....\......\DEMO_LCKSVD_LKDL_YaleB.m

....\......\featurevectors.mat

....\......\initialization4LCKSVD.m

....\......\labelconsistentksvd1.m

....\......\labelconsistentksvd2.m

....\......\LCKSVD_aux.asv

....\......\LCKSVD_aux.m

....\......\normcols.m

....\......\ksvdbox\Contents.m

....\......\.......\faq.txt

....\......\.......\ksvd.m

....\......\.......\ksvddemo.m

....\......\.......\ksvddenoise.m

....\......\.......\ksvddenoisedemo.m

....\......\.......\ksvdver.m

....\......\.......\odct2dict.m

....\......\.......\odct3dict.m

....\......\.......\odctdict.m

....\......\.......\odctndict.m

....\......\.......\ompdenoise.m

....\......\.......\ompdenoise1.m

....\......\.......\ompdenoise2.m

....\......\.......\ompdenoise3.m

....\......\.......\readme.txt

....\......\.......\showdict.m

....\......\.......\images\barbara.png

....\......\.......\......\boat.png

....\......\.......\......\house.png

....\......\.......\......\lena.png

....\......\.......\......\peppers.png

....\......\.......\private\addtocols.c

....\......\.......\.......\addtocols.m

....\......\.......\.......\addtocols.mexw64

....\......\.......\.......\add_dc.m

....\......\.......\.......\col2imstep.c

....\......\.......\.......\col2imstep.m

....\......\.......\.......\col2imstep.mexw64

....\......\.......\.......\collincomb.c

....\......\.......\.......\collincomb.m

....\......\.......\.......\collincomb.mexw64

....\......\.......\.......\countcover.m

....\......\.......\.......\dictdist.m

....\......\.......\.......\im2colstep.c

....\......\.......\.......\im2colstep.m

....\......\.......\.......\im2colstep.mexw64

....\......\.......\.......\imnormalize.m

....\......\.......\.......\iswhole.m

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