cs404 pattern recognition - locality preserving projections

CS404 : Pattern Recognition

Locality Preserving Projections

07-November-2016

Presenters

P Jishnu Jaykumar

201352005@iiitvadodara.ac.in

Paste your photo here

Vivek Kumar Singh

201352015@iiitvadodara.ac.in

Paper Overview

Authors● Xiaofei He and ● Partha Niyogi

From● Computer Science Department● The University of Chicago● Chicago, IL 60615

Resource Link

Before proceeding, 2 simple questions

1. Has anyone of you ever heard about dimensionality reduction techniques ?

2. If yes, then do you know why they are used ?

Dimensionality reduction (In CS/IT context)

● What is it ?○ In machine learning and statistics, dimensionality reduction or

dimension reduction is the process of reducing the number of random variables under consideration, via obtaining a set of principal variables.

■ Courtesy : https://en.wikipedia.org/wiki/Dimensionality_reduction

○ Simply, removing the redundant information(among the random variables) and keeping the important information (principal variables) that will be sufficient enough to represent the original data.

Dimensionality reduction (In CS/IT context)

● Why is it needed ?◆ It helps in data compressing and reducing the storage space required.

◆ It fastens the time required for performing same computations. Less dimensions leads to less

computing, also less dimensions can allow usage of algorithms that are unfit for a large number of

dimensions.

◆ It takes care of multicollinearity that improves the model performance. It removes redundant features.

For example: there is no point in storing a value in two different units (meters and inches).

● Don’t throw tomatoes towards us. This is just an example for the convenience of explanation. ..

Some common DR Techniques.

1. Multidimensional scaling

2. Linear discriminant analysis

3. High Correlation

4. Backward feature elimination

5. Factor Analysis

6. Missing Values

7. Low Variances

8. Principal Component Analysis (PCA)

9. And many more ...

To learn more about this techniques Click here.

Our topic : Locality Preserving Projection (LPP)

● An overview○ It is one of the DR techniques.○ Obviously, it is the topic of our presentation as well as

the topic of the research paper which we read.○ As the name suggests, this technique preserves the

information of its local region and thereby provides a helping hand in dimensionality reduction.

➔

Locality Preserving Projection (LPP)

● Algorithm ◆ .

Constructing the adjacency graph

Constructing the adjacency graphA Graphical look

Choosing the weights

EigenMaps

EigenMaps - Continues...

Evaluating criteria

Comparison between PCA and LPP.

Any Queries?

References

● http://www.machinelearning.org/proceedings/icml2005/papers/036_Statisti

cal_HeEtAl.pdf

● http://papers.nips.cc/paper/2359-locality-preserving-projections.pdf

● https://www.youtube.com/watch?v=BgMFBqrtCwo

Thank you ...