CSCI933代写、代做Algorithms语言、Python代写、代做Python程序设计

日期：2020-04-28 10:36

CSCI433/CSCI933: Machine Learning - Algorithms and

Applications

Assignment Problem Set #2

Lecturer: Prof. Philip O. Ogunbona(philipo@uow.edu.au)

School of Computing and Information Technology

University of Wollongong

Due date: Saturday May 2, 6:00 p.m.

Introduction

Often the number of features collected in a machine learning problem is very large and can be represented

as data in a large dimensional vector space. For instance one may need to solve a classification

problem and the number of features collected may number in several hundreds or thousands. Hence

the feature vector will be of a high dimension. Dealing with such data could be problematic because

of the so-called curse of dimensionality. It may be the case that the information required to characterize

the classification problem can be represented with a feature vector of much smaller dimension.

In this situation the information characterising the problem lies in a low-dimensional manifold of the

original vector space. The problem of dimensionality reduction is how to find this low-dimensional

manifold.

In this assignment, you will study some of the non-linear dimensionality reduction methods (van

der Maaten, Postma, & van den Herik, 2008) used in machine learning. You are to read, study,

understand and replicate aspects of the paper by van der Maaten et al. (2008). The assignment gives

you opportunity to generate and visualize artificial data and to work with both artificial and natural

dataset. You will use the Python programming language and the libraries available for machine

learning (scikit-learn), plotting and visualization (e.g. matplotlib, seaborn, etc.) to explore some of

the methods of dimensionality reduction. You will be aiming to replicate the results obtained by the

authors of the paper cited as (van der Maaten et al., 2008). There is also an extended version of

the paper that describes how the artificial data was generated (van der Maaten, Postma, & van den

Herik, 2009). This should help you when implementing code to generate the data. The two papers

are included in the specification pack provided for this assignment.

What needs to be done

1. Read, study and understand the two papers. You are replicating the short paper (van der

Maaten et al., 2008). The longer paper describes how to generate the artificial datasets and

includes more details about the techniques.

2. Generate and plot (visualize) the artificial datasets Swiss roll, Broken Swiss and Helix.

See for example Fig. 4 in van der Maaten et al. (2008). You will include the plot you generated

in your report and write about it.

3. Download and prepare to use the natural datasets: MNIST and Olivetti faces. You can use

the scikit-learn module in Python to download the MNIST and Olivetti faces datasets as

shown in this code snippet (or read the scikit-learn documentation).

import sklearn

from sklearn import datasets

from sklearn.datasets import fetch_openml

mnist_data = fetch_openml(’mnist_784’, version=1, return_X_y=True)

olivetti_faces = sklearn.datasets.fectch_olivetti_faces

Ensure that you really understand the organisation of the datasets. This is absolutely important

- check the size, shape, etc.

4. Using Python programming language, implement the dimensionality reduction methods: PCA,

Kernel PCA, Autoencoders, LLE (see Table 2 in van der Maaten et al. (2008)) as described in

the paper. Use the parameter settings provided in the paper. As a hint, these techniques are

implemented in the scikit-learn Python machine learning library.

5. Using generalization errors of 1-Nearest Neighbour classifier trained on the datasets, compare

the performance of the dimensionality reduction methods mentioned in item (4) above. Your

results will be presented as in Table. 4 of the paper for the datasets listed in items (2 and 3)

above.

6. Your report will be presented in a conference paper format (see accompanying template) and

should detail your understanding of theory of the techniques and experiments in the assigned

paper. You will describe the techniques in your own words with appropriate equations. When

you write an equation, the meaning of the symbols must be explained as well as the intuition

behind the equation itself. Your report MUST not be more than nine (9) pages in the format

specified by the template.

7. Please cite any other paper or book you have read in gaining deeper understanding of the

concepts and methods.

What needs to be submitted

• You will prepare a “zip” or “rar” file containing your report (9-page PDF file) and Python

code (named : “dim reduc.py”) file.

• Your code must run from command line as:

python3 dim_reduc.py

and write your results to standard output (stdout).

• Submit the “zip” or “rar” via Moodle dropbox provided on or before the deadline.

References

van der Maaten, L. J. P., Postma, E. O., & van den Herik, H. J. (2008). Dimensionality

reduction : A comparative review. online. Retrieved March 2020, from

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.112.5472&rep=rep1&type=pdf

2

van der Maaten, L. J. P., Postma, E. O., & van den Herik, H. J. (2009). Dimensionality

reduction : A comparative review. online. Retrieved March 2020, from

https://lvdmaaten.github.io/publications/papers/TR Dimensionality Reduction Review 2009.pdf

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