Project Proposal (PDF) - Oxford Brookes University

FP7-ICT-2011-9 STREP proposal
18/01/12 v1 [Dynact]
Work package description
Work package number 2 Start date or starting event: Month 1
Work package title Classification of generative dynamical models
Activity type 13 RTD
Participant number 1 2 3
Participant short name OBU IDSIA SYSTeMS
Person-months per participant 26 36 10
Objectives
The goal of this WP is the development of novel classification techniques for generative (dynamical)
models, in both their precise (traditional) and imprecise versions. This involves:
– the study of similarity measures for both precise and imprecise-probabilistic dynamical models, by
modeling similarity between sets of probability distributions (and specializing such measures to the case of
imprecise HMMs), and developing techniques for the supervised learning of similarity measures for various
classes of precise and imprecise dynamical models;
– the study of different alternative strategies for the classification of dynamical models, such as: distance
based clustering using the developed similarity measures; kernel SVM classification of imprecise models in
a structured learning framework; the application of compressed sensing techniques.
Description of work
Task 2.1 – Dissimilarity measures for imprecise graphical models. Different alternatives are explored.
first option is to consider imprecise graphical models, and iHMMs in particular, as convex sets of
distributions themselves. Results need to be specialised to the sets of (joint) probability associated with an
iHMM. More sensibly, when possessing a training set of labelled video sequences/models we can learn in a
supervised way the “best” similarity measure to adopt, by optimising classification performance over a
parameterised search space of metric. The pullback metric framework of differential geometry can be
exploited to this purpose. Such techniques can be generalized to imprecise-probabilistic graphical models,
once we define a base metric/similarity for them.
Task 2.2 – Classification of imprecise graphical models. Distance functions between convex sets of
probabilities (Task 2.1) can be used in a k-nearest-neighbor or other distance-based classifiers to classify
arbitrary imprecise models representing credal sets. Different approaches to pursue in parallel are the use of
kernels for imprecise-probabilistic models in a structured learning framework, and the exploration of
compressed sensing techniques based on randomised projection to tackle the problem.
Deliverables
D2.1 A scientific report on similarity metrics for imprecise probabilistic models represented as convex sets
of distributions, and their specialisation to the case of imprecise HMMs (month 10).
D2.2 Their efficient algorithmic implementation as a software prototype (month 12).
D2.3 A scientific report detailing the approach based on similarity measures to the classification of
imprecise graphical models (month 18).
D2.4 A report describing supervised metric learning techniques for precise/imprecise dynamical generative
models (month 16).
D2.5 Their efficient implementation as a software prototype (month 18).
D2.6 A scientific report detailing a supervised classification algorithm based on SVMs on imprecise models
as complex objects in a structured learning approach (month 28).
D2.7 A software prototype with its implementation (month 30).
D2.8 A scientific report detailing the proposed compressed sensing approach to the classification of
imprecise graphical models (month 34).
D2.9 A software prototype with its implementation (month 36).
13 Please indicate one activity per work package: RTD = Research and technological development; DEM =
Demonstration; MGT = Management of the consortium.
Proposal Part B: page [29] of [67]