Lec14.pptx - EM and GMM ECE-5424G CS-5824 Jia-Bin Huang Virginia Tech Spring 2019 Administrative • HW 3 due March 27 • Final project discussion Link

Lec14.pptx - EM and GMM ECE-5424G CS-5824 Jia-Bin Huang...

This preview shows page 1 - 15 out of 51 pages.

EM and GMM Jia-Bin Huang Virginia Tech Spring 2019 ECE-5424G / CS-5824
Image of page 1

Subscribe to view the full document.

Administrative HW 3 due March 27. Final project discussion: Link Final exam date/time Exam Section: 14M https:// banweb.banner.vt.edu/ssb/prod/hzskexam.P_DispExamInfo 2:05PM to 4:05PM May 13
Image of page 2
J. Mark Sowers Distinguished Lecture Michael Jordan Pehong Chen Distinguished Professor Department of Statistics and Electrical Engineering and Computer Sciences University of California, Berkeley 3/28/19 7:30 PM, McBryde 100
Image of page 3

Subscribe to view the full document.

K-means algorithm Input: (number of clusters) Training set (note: drop convention) Slide credit: Andrew Ng
Image of page 4
K-means algorithm Randomly initialize cluster centroids Repeat{ for = 1 to index (from 1 to ) of cluster centroid closest to for = 1 to average (mean) of points assigned to cluster } Cluster assignment step Centroid update step Slide credit: Andrew Ng
Image of page 5

Subscribe to view the full document.

K-means optimization objective Index of cluster (1, 2, … K) to which example is currently assigned cluster centroid () cluster centroid of cluster to which example has been assigned Optimization objective: Example: Slide credit: Andrew Ng
Image of page 6
K-means algorithm Randomly initialize cluster centroids Repeat{ for = 1 to index (from 1 to ) of cluster centroid closest to for = 1 to average (mean) of points assigned to cluster } Cluster assignment step Centroid update step Slide credit: Andrew Ng
Image of page 7

Subscribe to view the full document.

Hierarchical Clustering A hierarchy might be more nature Different users might care about different levels of granularity or even prunings. Slide credit: Maria-Florina Balcan
Image of page 8
Hierarchical Clustering Top-down (divisive) Partition data into 2-groups (e.g., 2-means) Recursively cluster each group Bottom-up (agglomerative) Start with every point in its own cluster. Repeatedly merge the “closest” two clusters Different definitions of “closest” give different algorithms. Slide credit: Maria-Florina Balcan
Image of page 9

Subscribe to view the full document.

Bottom-up (agglomerative) Have a distance measure on pairs of objects. Distance between and Single linkage: Complete linkage: Average linkage: Ward’s method Slide credit: Maria-Florina Balcan
Image of page 10
Bottom-up (agglomerative) Single linkage : At any time, distance between any two points in a connected components < r. Complete linkage : Keep max diameter as small as possible at any level Ward’s method Merge the two clusters such that the increase in k-means cost is as small as possible. Works well in practice Slide credit: Maria-Florina Balcan
Image of page 11

Subscribe to view the full document.

Things to remember Intro to unsupervised learning K-means algorithm Optimization objective Initialization and the number of clusters Hierarchical clustering
Image of page 12
Today’s Class Examples of Missing Data Problems Detecting outliers Latent topic models Segmentation Background Maximum Likelihood Estimation Probabilistic Inference Dealing with “Hidden” Variables EM algorithm, Mixture of Gaussians Hard EM
Image of page 13

Subscribe to view the full document.

Today’s Class Examples of Missing Data Problems
Image of page 14
Image of page 15
  • Spring '19

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern

Ask Expert Tutors You can ask 0 bonus questions You can ask 0 questions (0 expire soon) You can ask 0 questions (will expire )
Answers in as fast as 15 minutes