Event Location: NSH 1507
Bio: Sangmin is a PhD candidate in Collge of Computing at Georgia Institute
of Technology. He received his BS in computer science with cum laude
from Seoul National Univ, in 2003. During his PhD thesis work, Sangmin
has focussed on developing time-series models to address problems such
as continuous labeling, robust labeling for data with systematic
global variations, and hierarchical labeling, where he published his
work at major conferences and journals in computer vision and AI.
Additionally, he worked on problems in robotics, signal processing,
and graphics, where he co-authored several academic publications. He
was a recipient of Samsung Lee Kun Hee fellowship from ’03 to ’07. His
research interests include computer vision, machine learning,
robotics, computer graphics, data mining, time-series modeling and
computational linguistics.

Abstract: Automated analysis of temporal data is a task of utmost importance for
intelligent machines. For example, ubiquitous computing systems need
to understand the intention of humans from the stream of sensory
information, and health-care monitoring systems can assist patients
and doctors by providing automatically annotated daily health reports.
Moreover, a huge amount of multimedia data such as videos await to be
analyzed and indexed for search purposes, while scientific data such
as recordings of animal behavior and evolving brain signals are being
collected in the hope to deliver a new scientific discovery about life.
In this talk, we will describe a class of newly developed time-series
models in Bayesian network formulation. In particular, we will focus
on the extensions of switching linear dynamic systems (SLDSs) with
higher-order temporal structure and inference methods thereof. SLDSs
have been used to model continuous multivariate temporal data under
the assumption that the characteristics of complex temporal sequences
can be captured by Markov switching between a set of simpler
primitives which are linear dynamic systems (LDSs). In particular, we
will focus on the extensions of SLDSs which are developed to address
problems such as continuous labeling, robust labeling for data with
systematic global variations, and hierarchical labeling.

First, we will present a data-driven MCMC inference method for SLDS
model. The distinctive characteristic of this approach is that it
turns heuristic labeling methods into data-driven proposal
distributions of MCMC where the outcome results in a principled
approximate inference method. In other words, it is a methodology to
turn a novice into an expert. We show the resulting MCMC method for
SLDSs where an inference problem is now solved which could not be
addressed efficiently by Gibbs sampling previously.

Second, parametric SLDSs (P-SLDSs) explicitly model the global
parameters which induce systematic temporal and spatial variations of
data. The additional structure of PSLDSs allows us to conduct the
global parameter quantification task which could not be addressed by
standard SLDSs previously in addition to providing more accurate
labeling ability.

Third, segmental SLDSs (S-SLDSs) provide the ability to capture
descriptive duration models within LDS regimes. The encoded duration
models are more descriptive than the exponential duration models
induced within the standard SLDSs and allow us to avoid the severe
problem of over-segmentations and demonstrate superior labeling
accuracy.

Finally, we introduce hierarchical SLDSs (H-SLDSs), a generalization
of standard SLDSs with hierarchic Markov chains. H-SLDSs are able to
encode temporal data which exhibits hierarchic structure where the
underlying low-level temporal patterns repeatedly appear in different
higher level contexts. Accordingly, H-SLDSs can be used to analyze
temporal data at multiple temporal granularities, and provide the
additional ability to learn a more complex H-SLDS model easily by
combining underlying H-SLDSs.

The developed SLDS extensions have been applied to two real-world
problems. The first problem is to automatically analyze the honey bee
dance dataset where the goal is to correctly segment the dance
sequences into different regimes and parse the messages about the
location of food sources embedded in the data. We show that a
combination of the P-SLDS and S-SLDS models has demonstrated improved
labeling accuracy and message parsing results. The second problem is
to analyze the wearable exercise data where we aim to provide an
automatically generated exercise record at multiple temporal
resolutions.