1. Brian Clarkson’s Life Patterns, Ch 6-7
cse 574
11 Feb 2004

2. Situation Classification
Situation = abstract place
home, neighborhood, office, store, restaurant, meeting, …
“Context free” classification
measure similarity between individual 25-frame “chunks”, by calculating log-likelihood of their alignment What’s that?
Good performance within a day, poor between days – 56% vs. 72%

3. Adding Context Classification with long-term context Strategy
align chunks across 30 days of data
73% avg. accuracy across different days
Strategy
calculate CF matches
if best match in same day accept, else calculate best long-term match
85% accuracy

5. Perplexity
Perplexity – used in speech recognition as a measure of how strongly previous context predicts the next word
Application to Life Patterns:
segment sensor stream
cluster similar segments
create a 1st order Markov model between clusters

6. Issue: Number of Clusters
How many clusters (situations) to use?
Measuring accuracy (strength) of a model?
compare ground truth at time t against most likely prediction given ground truth for time t-1
gives simple accuracy / granularity tradeoff
better: mutual information between pairs of adjacent symbols in training data
MI is relative to the particular model
measured in bits

8. Why do this?

9. Is Life Complex? 50% of the time perplexity is < 4
“A person’s life is not an ever-expanding list of unique situations.”
Is this a statement about life, or about the experimental setup?
What is the role of novelty in the life of any organism?