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

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%

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

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

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

Why do this?

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?