代做kernel PCA、代写Python,C/C++编程语言、代做Python,C/C++、dataset代写-代写C/C++编程

You are required to write code for performing PCA and kernel PCA (KPCA) on

a given dataset. After performing PCA/KPCA, you will use various classifiers

to perform classification. You will investigate the effect of applying PCA,

KPCA and KPCA with different types of kernels. Specifically, the tasks you

need to complete are:

• Write code for performing PCA

• Apply PCA to the given dataset and then perform classification with the

nearest neighbour classifier. Analyse the performance change against

different 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 the

lecture slides)

• Apply KPCA to the given dataset and then perform classification with nearest

neighbour classifier. Analyse the performance change against different

reduced dimensions. (suggestion: from 256 to 10)

You can choose either Matlab, Python, or C/C++. I would personally suggest

Matlab or Python.

The PCA and KPCA part of your code should not rely on any 3rd-party toolbox.

Only Matlab's built-in API's or Python/ C/C++'s standard libraries are

allowed. party implementation of linear SVM for your experiments.

You are also required to submit a report (<10 pages in PDF format), which

should have the following sections (report contributes 50% to the mark; code

50%):

• An algorithmic description of PCA and KPCA. (5%) • Your understanding of

PCA and KPCA (anything that you believe is relevant to this algorithm) (5%)

• Some analyses of your implementation. You should plot an error curve against

the number of reduced dimensions. For KPCA, results from different kernels

should be plotted on the same figure with different colours. (20% for master

students and 40% for undergraduate students)