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@Saurabh7
Created March 23, 2014 15:15
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{
"metadata": {
"name": "MKL"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Multiple Kernel Learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<em>Multiple kernel learning</em> is about using a combined kernel i.e. a kernel consisting of a linear combination of arbitrary kernels over different domains. The coefficients or weights of the linear combination can be learned as well. We will see how to construct a combined kernel, determine kernel weights and use them for predictions."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# import all shogun classes\n",
"from modshogun import *\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Introduction:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kernel based methods such as support vector machines (SVMs) employ a so-called kernel function $k(x_{i},x_{j})$ which intuitively computes the similarity between two examples $x_{i}$ and $x_{j}$. </br>\n",
"Selecting the kernel function\n",
"$k()$ and its parameters is an important issue in training. Kernels designed by humans usually capture one aspect of data. Choosing one kernel means to select exactly one such aspect. Which means combining such ascpects is often better than selecting.\n",
"</br>So in a svm, defined as:\n",
"$$f({\\bf x})=sign\\left(\\sum_{i=0}^{N-1} \\alpha_i k({\\bf x}, {\\bf x_i})+b\\right)$$</brr>\n",
"\n",
"\n",
"One could make a combination of kernels like:\n",
"$${\\bf k}(x_i,x_j)=\\sum_{k=0}^{K} \\beta_k {\\bf k_k}(x_i, x_j)$$\n",
"where $\\beta_k > 0$ and $\\sum_{k=0}^{K} \\beta_k = 1$\n"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Prediction using MKL in shogun:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shogun provides an easy way to make combination of kernels using the [CombinedKernel](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CCombinedKernel.html) class, to which we can append any [Kernel](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKernel.html) from the many options shogun provides."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kernel = CombinedKernel()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To see the preiction capabilities, lets generate some data using the [GMM](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CGMM.html) class. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num=30;\n",
"dist=1.0;\n",
"\n",
"gmm=GMM(4)\n",
"gmm.set_nth_mean(array([-dist,dist]),0)\n",
"gmm.set_nth_mean(array([2*dist,-1.5*dist]),1)\n",
"gmm.set_nth_mean(array([-dist,-3*dist]),2)\n",
"gmm.set_nth_mean(array([2*dist,dist]),3)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),0)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),1)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),2)\n",
"gmm.set_nth_cov(array([[1.0,0.0],[0.0,1.0]]),3)\n",
"gmm.set_coef(array([1.0,0.0,0.0,0.0]))\n",
"xntr=array([gmm.sample() for i in xrange(num)]).T\n",
"xnte=array([gmm.sample() for i in xrange(5000)]).T\n",
"gmm.set_coef(array([0.0,1.0,0.0,0.0]))\n",
"xntr1=array([gmm.sample() for i in xrange(num)]).T\n",
"xnte1=array([gmm.sample() for i in xrange(5000)]).T\n",
"gmm.set_coef(array([0.0,0.0,1.0,0.0]))\n",
"xptr=array([gmm.sample() for i in xrange(num)]).T\n",
"xpte=array([gmm.sample() for i in xrange(5000)]).T\n",
"gmm.set_coef(array([0.0,0.0,0.0,1.0]))\n",
"xptr1=array([gmm.sample() for i in xrange(num)]).T\n",
"xpte1=array([gmm.sample() for i in xrange(5000)]).T\n",
"traindata=concatenate((xntr,xntr1,xptr,xptr1), axis=1)\n",
"trainlab=concatenate((-ones(2*num), ones(2*num)))\n",
"\n",
"testdata=concatenate((xnte,xnte1,xpte,xpte1), axis=1)\n",
"testlab=concatenate((-ones(10000), ones(10000)))\n",
"\n",
"feats_train=RealFeatures(traindata) #convert to shogun features\n",
"labels=BinaryLabels(trainlab) #generate labels for data"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_=jet()\n",
"_=scatter(traindata[0,:], traindata[1,:], c=trainlab, s=100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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H49nTh4j09LFer2fG3LlpKlPRrVMnzEuXktjYjFUBAYycPp33338/TezyhqyF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\nFwRByKNEAhcEQcijRAIXBEHIo/4fYsBIc9YWypIAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to help us visualize lets use two gaussian kernels ([CGaussianKernel](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CGaussianKernel.html)) with considerably different widths. We need to append them to the Combined kernel. To generate the optimal weights (i.e $\\beta$s in the above equation), training of [MKL](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMKLClassification.html) is required. This generates the weights as seen in this example."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"width0=0.5\n",
"kernel0=GaussianKernel(feats_train, feats_train, width0)\n",
"\n",
"width1=25\n",
"kernel1=GaussianKernel(feats_train, feats_train, width1)\n",
"\n",
"kernel.append_kernel(kernel0) #combine kernels\n",
"kernel.append_kernel(kernel1)\n",
"kernel.init(feats_train, feats_train)\n",
"\n",
"mkl = MKLClassification()\n",
"mkl.set_mkl_norm(1)\n",
"mkl.set_C(1, 1)\n",
"mkl.set_kernel(kernel)\n",
"mkl.set_labels(labels)\n",
"\n",
"mkl.train() #train to get weights\n",
"\n",
"w=kernel.get_subkernel_weights()\n",
"print w"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.94176122 0.05823878]\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The weights generated can be intuitively understood too. We will see that on plotting individual subkernels outputs and and outputs of the MKL classification. To apply on test features, we can reinitialize the kernel with `kernel.init` and pass the test features. After that its just a matter of doing `mkl.apply` to generate outputs. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"size=100\n",
"x1=linspace(-5, 5, size)\n",
"x2=linspace(-5, 5, size)\n",
"x, y=meshgrid(x1, x2)\n",
"grid=RealFeatures(array((ravel(x), ravel(y)))) # generate X-Y grid test data\n",
"\n",
"kernel0t=GaussianKernel(feats_train, grid, width0)\n",
"kernel1t=GaussianKernel(feats_train, grid, width1)\n",
"\n",
"kernelt=CombinedKernel()\n",
"kernelt.append_kernel(kernel0t)\n",
"kernelt.append_kernel(kernel1t)\n",
"kernelt.init(feats_train, grid) #initailize with test grid\n",
"\n",
"mkl.set_kernel(kernelt)\n",
"grid_out=mkl.apply() #prediction\n",
"\n",
"z=grid_out.get_values().reshape((size, size))\n",
"\n",
"figure(figsize=(10,5))\n",
"title(\"Classification using MKL\")\n",
"c=pcolor(x, y, z)\n",
"_=contour(x, y, z, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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onGgVhEurXOLvVzmuAA93LMTfCamXQf4c2NERUm6F5BvBFv7BjBUYlT+UJBLh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\nEEKIqJMBhhBCCCGi7n9ZwNVVsCOrnQAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To justify the weights, lets train and compare two subkernels with the MKL classification output. Training MKL classifier with a single kernel appended to a combined kernel makes no sense and is just like normal single kernel based classification, but lets do it for comparison."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"z=grid_out.get_labels().reshape((size, size))\n",
"\n",
"figure(figsize=(20,5)) # MKL\n",
"subplot(131, title=\"Multiple Kernels combined\")\n",
"c=pcolor(x, y, z)\n",
"_=contour(x, y, z, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c)\n",
"\n",
"comb_ker0=CombinedKernel()\n",
"comb_ker0.append_kernel(kernel0)\n",
"comb_ker0.init(feats_train, feats_train)\n",
"mkl.set_kernel(comb_ker0)\n",
"mkl.train()\n",
"comb_ker0t=CombinedKernel()\n",
"comb_ker0t.append_kernel(kernel0)\n",
"comb_ker0t.init(feats_train, grid)\n",
"mkl.set_kernel(comb_ker0t)\n",
"out0=mkl.apply()\n",
"\n",
"z=out0.get_labels().reshape((size, size)) #subkernel 1\n",
"subplot(132, title=\"Kernel 1\")\n",
"c=pcolor(x, y, z)\n",
"_=contour(x, y, z, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c)\n",
"\n",
"comb_ker1=CombinedKernel()\n",
"comb_ker1.append_kernel(kernel1)\n",
"comb_ker1.init(feats_train, feats_train)\n",
"mkl.set_kernel(comb_ker1)\n",
"mkl.train()\n",
"comb_ker1t=CombinedKernel()\n",
"comb_ker1t.append_kernel(kernel1)\n",
"comb_ker1t.init(feats_train, grid)\n",
"mkl.set_kernel(comb_ker1t)\n",
"out1=mkl.apply()\n",
"\n",
"z=out1.get_labels().reshape((size, size)) #subkernel 2\n",
"subplot(133, title=\"kernel 2\")\n",
"c=pcolor(x, y, z)\n",
"_=contour(x, y, z, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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FwFI2AX+peMYFjAfc1gYsUkl5QsSBkjEDl+F9oB8m44hEhEPzRLx+hSgJ5Riw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\nCztvxk5HfgAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see the multiple kernel output seems just about right. Kernel 1 gives a sort of overfiting output while the kernel 2 seems not so accurate. The kernel weights are hence so adjusted to get a refined output. We can have a look at the errors by these subkernels to have more food for thought. Most of the times, the MKL error is lesser as it incorporates aspects of both kernels. One of them is strict while other is lenient, MKL finds a balance between those."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kernelt.init(feats_train, RealFeatures(testdata))\n",
"mkl.set_kernel(kernelt)\n",
"out=mkl.apply()\n",
"\n",
"evaluator=ErrorRateMeasure()\n",
"print \"Test error is %2.2f%% :MKL\" % (100*evaluator.evaluate(out,BinaryLabels(testlab)))\n",
"\n",
"\n",
"comb_ker0t.init(feats_train,RealFeatures(testdata)) \n",
"mkl.set_kernel(comb_ker0t)\n",
"out=mkl.apply()\n",
"\n",
"evaluator=ErrorRateMeasure()\n",
"print \"Test error is %2.2f%% :Subkernel1\"% (100*evaluator.evaluate(out,BinaryLabels(testlab)))\n",
"\n",
"comb_ker1t.init(feats_train, RealFeatures(testdata))\n",
"mkl.set_kernel(comb_ker1t)\n",
"out=mkl.apply()\n",
"\n",
"evaluator=ErrorRateMeasure()\n",
"print \"Test error is %2.2f%% :subkernel2\" % (100*evaluator.evaluate(out,BinaryLabels(testlab)))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Test error is 11.59% :MKL\n",
"Test error is 22.49% :Subkernel1"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Test error is 13.58% :subkernel2"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"MKL for knowledge discovery:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MKL can recover information about the problem at hand.Lets us see this with a binary classification problem. The task is to separate two concentric classes shaped like circles. By varying the distance between the boundary of the circles we can control the separability of the problem. Starting with an almost non-separable scenario , the data quickly becomes separable as the distance between the circles increases."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def circle(x, radius, neg):\n",
" y=sqrt(square(radius)-square(x))\n",
" if neg:\n",
" return[x, -y]\n",
" else:\n",
" return [x,y]\n",
" \n",
"def get_circle(radius):\n",
" neg=False\n",
" range0=linspace(-radius,radius,100)\n",
" pos_a=array([circle(i, radius, neg) for i in range0]).T\n",
" neg=True\n",
" neg_a=array([circle(i, radius, neg) for i in range0]).T\n",
" c=concatenate((neg_a,pos_a), axis=1)\n",
" return c\n",
"\n",
"def get_data(r1, r2):\n",
" c1=get_circle(r1)\n",
" c2=get_circle(r2)\n",
" c=concatenate((c1, c2), axis=1)\n",
" feats_tr=RealFeatures(c)\n",
" return c, feats_tr\n",
"\n",
"l=concatenate((-ones(200),ones(200)))\n",
"lab=BinaryLabels(l)\n",
"\n",
"c, feats_tr=get_data(2,4) #get 2 circles with radius 2 and 4\n",
"c1, feats_tr1=get_data(2,3)\n",
"_=gray()\n",
"figure(figsize=(10,5))\n",
"subplot(121)\n",
"p=scatter(c[0,:], c[1,:], c=lab)\n",
"subplot(122)\n",
"q=scatter(c1[0,:], c1[1,:], c=lab)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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UrFkz9ke7npw8eZLU1NRIV1eX1q9fTwMHDqSysjLy8vIiT09P6tu3L/H5fDp3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0nK5W1KSkrC/PnzYWBgAB0dHbi6uuLAgQNITEzE3LlzkZqaiu7du0NaWhpRUVFwcnKCtDR/hn9d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\nEOzfvx/e3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XHQ0NCAnZ1dl/mahI4kdG35q3oQHByM4OBghISEYPny5YiOju7Q8YG3V49e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\ngWKMMcYYa6f/A05wr0GMGwMfAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the type of circles we want to distinguish between. We can try classification with a constant separation between the circles first."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def train_mkl(circles, feats_tr):\n",
" kernel0=GaussianKernel(feats_tr, feats_tr, 1) # four kernels with different widths \n",
" kernel1=GaussianKernel(feats_tr, feats_tr, 5)\n",
" kernel2=GaussianKernel(feats_tr, feats_tr, 7)\n",
" kernel3=GaussianKernel(feats_tr, feats_tr, 10)\n",
" kernel = CombinedKernel()\n",
" kernel.append_kernel(kernel0)\n",
" kernel.append_kernel(kernel1)\n",
" kernel.append_kernel(kernel2)\n",
" kernel.append_kernel(kernel3)\n",
" \n",
" kernel.init(feats_tr, feats_tr)\n",
" mkl = MKLClassification()\n",
" mkl.set_mkl_norm(1)\n",
" mkl.set_C(1, 1)\n",
" mkl.set_kernel(kernel)\n",
" mkl.set_labels(lab)\n",
" \n",
" mkl.train()\n",
" \n",
" w=kernel.get_subkernel_weights()\n",
" return w, mkl\n",
"\n",
"def test_mkl(mkl, grid):\n",
" kernel0t=GaussianKernel(feats_tr, grid, 1)\n",
" kernel1t=GaussianKernel(feats_tr, grid, 5)\n",
" kernel2t=GaussianKernel(feats_tr, grid, 7)\n",
" kernel3t=GaussianKernel(feats_tr, grid, 10)\n",
" kernelt = CombinedKernel()\n",
" kernelt.append_kernel(kernel0t)\n",
" kernelt.append_kernel(kernel1t)\n",
" kernelt.append_kernel(kernel2t)\n",
" kernelt.append_kernel(kernel3t)\n",
" kernelt.init(feats_tr, grid)\n",
" mkl.set_kernel(kernelt)\n",
" out=mkl.apply()\n",
" return out\n",
"\n",
"size=50\n",
"x1=linspace(-10, 10, size)\n",
"x2=linspace(-10, 10, size)\n",
"x, y=meshgrid(x1, x2)\n",
"grid=RealFeatures(array((ravel(x), ravel(y))))\n",
"\n",
"\n",
"w, mkl=train_mkl(c, feats_tr)\n",
"print w\n",
"out=test_mkl(mkl,grid)\n",
"\n",
"z=out.get_values().reshape((size, size))\n",
"\n",
"figure(figsize=(5,5))\n",
"c=pcolor(x, y, z)\n",
"_=contour(x, y, z, linewidths=1, colors='black', hold=True)\n",
"_=colorbar(c)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 3.70179551e-05 6.14303709e-01 3.83958304e-01 1.70096833e-03]\n"
]
},
{
"output_type": "display_data",
"png": 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8GX5+fgqz50BpaSlWrFiBbdu2kRbX19fXY8WKFbCwsMC8efMID1V9fT0iIiJw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\ndoQQWfg/hd7ZQYGHl5AAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So MKL classifier classifies them as expected. Now lets vary the separation and see how it affects the weights.The choice of the kernel width of the Gaussian kernel used for classification is expected to depend on the separation distance of the learning problem: An increased distance between the circles will correspond to a larger optimal kernel width. This effect should be visible in the results of the MKL, where we used MKL-SVMs with four kernels with different widths (1,5,7,10). "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range1=linspace(5.5,7.5,50)\n",
"x=linspace(1.5,3.5,50)\n",
"temp=[]\n",
"\n",
"for i in range1:\n",
" c, feats=get_data(4,i) #vary separation between circles\n",
" w, mkl=train_mkl(c, feats)\n",
" temp.append(w)\n",
"y=array([temp[i] for i in range(0,50)]).T\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(20,5))\n",
"_=plot(x, y[0,:], color='k', linewidth=2)\n",
"_=plot(x, y[1,:], color='r', linewidth=2)\n",
"_=plot(x, y[2,:], color='g', linewidth=2)\n",
"_=plot(x, y[3,:], color='y', linewidth=2)\n",
"title(\"Comparison between kernel widths and weights\")\n",
"ylabel(\"Weight\")\n",
"xlabel(\"Distance between circles\")\n",
"_=legend([\"1\",\"5\",\"7\",\"10\"])\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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WNXRviB7BPdAzuCcaV3/8KAl7K3t0CuiETgGdAAC5mlycuH0CB2P15dLRm0dx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\nljl2xAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above plot we see the obtained kernel weightings for the four kernels. Every line shows one weighting. The courses of the kernel weightings reflect the development of the learning problem: as long as the problem is difficult the best separation can be obtained when using the kernel with smallest width. The low width kernel looses importance when the distance between the circle increases and larger kernel widths obtain a larger weight in MKL. Increasing the distance between the circles, kernels with greater widths are used. "
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Mathematical formulation:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In MKL $\\alpha_i$,$\\beta$ and bias are determined by solving the following optimization program\n",
"\n",
"$$\\mbox{min} \\hspace{4mm} \\gamma-\\sum_{i=1}^N\\alpha_i$$\n",
"$$ \\mbox{w.r.t.} \\hspace{4mm} \\gamma\\in R, \\alpha\\in R^N \\nonumber$$\n",
"$$\\mbox {s.t.} \\hspace{4mm} {\\bf 0}\\leq\\alpha\\leq{\\bf 1}C,\\;\\;\\sum_{i=1}^N \\alpha_i y_i=0 \\nonumber$$\n",
"$$ \\frac{1}{2}\\sum_{i,j=1}^N \\alpha_i \\alpha_j y_i y_j \\forall k=1,\\ldots,K\\nonumber\\\\\n",
"$$\n",
"\n",
"\n",
" here C is a pre-specified regularization parameter\n",
"Within shogun this optimization problem is solved using [semi-infinite programming](http://en.wikipedia.org/wiki/Semi-infinite_programming). For 1-norm MKL using one of the two approaches described in\n",
"[1].\n",
"The first approach (also called the wrapper algorithm) wraps around a single kernel SVMs, alternatingly solving for \u03b1 and \u03b2. It is using a traditional SVM to generate new violated constraints and thus requires a single kernel SVM and any of the SVMs contained in shogun can be used. In the MKL step either a linear program is solved via glpk or cplex or analytically or a newton (for norms>1) step is performed.\n",
"\n",
"The second much faster but also more memory demanding approach performing interleaved optimization, is integrated into the chunking-based SVMlight.\n",
"\n"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"References:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1] Soeren Sonnenburg, Gunnar Raetsch, Christin Schaefer, and Bernhard Schoelkopf. Large Scale Multiple Kernel Learning. Journal of Machine Learning Research, 7:1531-1565, July 2006.\n",
"\n",
"[2] Kernel Methods for Object Recognition , Christoph H. Lampert"
]
}
],
"metadata": {}
}
]
}
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