讲解：kernel PCA、Python,C/C++、Python,C/C++、datasetR|SQ

You are required to write code for performing PCA and kernel PCA (KPCA) ona given dataset. After performing PCA/KPCA, you will use various classifiersto perform classification. You will investigate the effect of applying PCA,KPCA and KPCA with different types of kernels. Specifically, the tasks youneed to complete are:• Write code for performing PCA• Apply PCA to the given dataset and then perform classification with thenearest neighbour classifier. Analyse the performance change againstdifferent reduced dimensions. (suggestion: from 256 to 10)• Write code for performing KPCA. Use three kernel functions, Linear Kernel(equivalent to PCA), Gaussian-RBF kernel and Hellinger kernel (See thelecture slides)• Apply KPCA to the given dataset and then perform classification with nearestneighbour classifier. Analyse the performance change against differentreduced dimensions. (suggestion: from 代做kernel PCA、代写Python,C/C++编程语256 to 10) You can choose either Matlab, Python, or C/C++. I would personally suggestMatlab or Python.The PCA and KPCA part of your code should not rely on any 3rd-party toolbox.Only Matlabs built-in APIs or Python/ C/C++s standard libraries areallowed. party implementation of linear SVM for your experiments.You are also required to submit a report (should have the following sections (report contributes 50% to the mark; code50%):• An algorithmic description of PCA and KPCA. (5%) • Your understanding ofPCA and KPCA (anything that you believe is relevant to this algorithm) (5%)• Some analyses of your implementation. You should plot an error curve againstthe number of reduced dimensions. For KPCA, results from different kernelsshould be plotted on the same figure with different colours. (20% for masterstudents and 40% for undergraduate students) 转自：http://www.3daixie.com/contents/11/3444.html