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.

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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