A GIAN Course on
Principal Components Analysis (PCA) and Robust PCA for Modern Datasets: Theory, Algorithms, and Applications
December 18 - 22, 2017
Sponsored by: MHRD, Govt. of India
Organized by: ECE Department, IIITD

Register Here

Participation certificates will be awarded to all participants. Last day of registration has been extended till 1st December, 2017.
This registration deadline of December 1 will not be extended further.

Overview

Objectives

The primary objectives of this course are the following:
  1. i. To introduce the original PCA problem, the eigenvalue decomposition (EVD) solution, the guarantees for large n.
  2. ii. To provide understanding of the correlated-PCA problem, solutions and guarantees - what is known and what is not.
  3. iii. To provide exposure to streaming or online PCA, robust PCA and streaming dynamic robust PCA.

Who should attend the course

  1. 1. You would like to get introduced to recent and ongoing research in statistical Machine Learning, AND
  2. 2. You have sufficient mathematical maturity (have taken at least an undergraduate level course in linear algebra and probability)
  3. 3. Typical Audience
    1.     i. M.Tech or Ph.D. student in Mathematics or Applied Mathematics, Statistics, Computer Science or Communications and Signal Processing (within Electrical / Electronics Engineering deptt.)
    2.     ii. Faculty member or lecturer or industry researcher in above area
    3.     iii. An enthusiastic and mathematically mature final year B.Sc or B.Tech student with sufficient background For a sampler of some of above topics, see here

Fee

The participation fees per person for attending the course is as follows:
Students : Rs 500
Faculty : Rs 1000
Industry/ Research Organizations: Rs 2000
The above fees includes all instructional materials and free internet facility.
Please Note: Food and accommodation will be charged separately.

Modules and Time Table

Day 1: December 18, 2017

  • 09:30 am to 11:00 am Lecture 1: Introduction of course and review of probability theory
  • 11:00 am to 11:30 Tea
  • 11:30 am to 12:30 pm Lecture 2: Review of probability theory contd.
  • 12:30 pm to 02:00 pm Lunch
  • 02:00 pm to 03:30 pm Lecture 3: Basic linear algebra
  • 03:30 pm to 04:00 pm Tea
  • 04:00 pm to 05.30 pm Lecture 4: Basics of optimization theory

Day 2: December 19, 2017

  • 09:30 am to 11:00 am Lecture 5: Davis Kahan sin theta theorem and random matrix theory.
  • 11:00 am to 11:30 am Tea
  • 11:30 am to 12:30 pm Lecture 6: Davis Kahan sin theta theorem and random matrix theory contd.
  • 12:30 pm to 02:00 pm Lunch
  • 02:00 pm to 03:00 pm Lecture 7: Davis Kahan sin theta theorem and random matrix theory cont.
  • 03:00 pm to 03:30 pm Tea
  • 03:30 pm to 05:00 pm Lecture 8&9: PCA and correlated PCA (PCA when data and corrupting noise are correlated).

Day 3: December 20, 2017

  • 09:30 am to 11:00 am Lecture 10:Optimization theory and algorithms.
  • 11:00 am to 11:30 am Tea
  • 11:30 am to 01:00 pm Lecture 11: Streaming PCA or Online PCA.
  • 01:00 pm to 02:00 pm Lunch
  • 02:00 pm to 03:00 pm Lecture12: Key ideas from sparse recovery and ell-1 minimization.
  • 03:00 pm to 03:30 pm Tea
  • 03.30 pm to 05.30 pm Lecture 13: Streaming or dynamic robust PCA or robust sparse recovery- non-convex solutions.

Day 4: December 21, 2017

  • 09:30 am to 11:00 am Lecture 14: Convex and non-convex solutions to robust PCA.
  • 11:00 am to 11:30 am Tea
  • 11:30 am to 12:30 pm Lecture 15: A correlated-PCA based reformulation of streaming RPCA.
  • 12:30 pm to 02:00 pm Lunch
  • 02:00 pm to 03:30 pm Lecture 16: General structure matrix demixing.
  • 03:30 pm to 04:00 pm Tea
  • 04:00 pm to 05:00 pm Lab

Day 5: December 22, 2017

  • 09:30 am to 11:30 am Quizzes/Lab/Queries
  • 11:30 am to 12:00 noon Tea
  • 12:00 noon to 01:00 pm valedictory closing
  • 01:00 pm to 02:00 pm Lunch

Faculty Coordinators

Anubha Gupta Anubha Gupta received her B.Tech and M.Tech from Delhi University, India in 1991 and 1997 in Electronics and Communication Engineering. She received her PhD. from Indian Institute of Technology (IIT), Delhi, India in 2006 in Electrical Engineering. She did her second Master’s as a full time student from the University of Maryland, College Park, USA from 2008-2010 in Education with concentration: Higher Education Leadership and Policy Studies. She worked as Assistant Director with the Ministry of Information and Broadcasting, Govt. of India (through Indian Engineering Services) from 1993 to 1999 and, as faculty at NSIT-Delhi (2000-2008) and IIIT-Hyderabad (2011-2013), India. Since Dec. 2013, she is working as Associate Professor at IIIT-Delhi.
Apart from this, she worked in USA from 2009 to 2011- first as a researcher (in education) at University System of Maryland in the office of Associate Vice-Chancellor, Academics, and later as Director of Assessment in the office of the Provost at Bowie State University, Maryland, USA. Her current research interests include biomedical signal and image processing including fMRI, MRI, EEG, ECG signal processing, genomics signal processing in cancer research, Wavelets in deep learning, wavelet transform and applications, and signal processing for communication engineering.
Dr. Gupta is a senior member of IEEE Signal processing Society and a member of IEEE Women in Engineering society. She is an expert member of ECE for National Board of Accreditation (NBA), India responsible for the accreditation of UG & PG programs of Engineering in India. She has been working as UG chair at IIIT-Delhi since July 2016. Dr. Gupta hosted a symposium (along with Dr. Selin Aviyente, MSU and Dr. Namrata Vaswani, ISU) on “Big Data Analysis and Challenges in Medical Imaging” at IEEE GlobalSIP 2016 held in Washington DC, USA from Dec. 7 - 9, 2016.