cs404 pattern recognition - locality preserving projections
TRANSCRIPT
CS404 : Pattern Recognition
Locality Preserving Projections
07-November-2016
Presenters
P Jishnu Jaykumar
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Vivek Kumar Singh
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.
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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 ...