Matrix analysis
System identification: theory for the user
System identification: theory for the user
Finite dimensional filters for a class of nonlinear systems and immersion in a linear system
SIAM Journal on Control and Optimization
Subspace algorithms for the stochastic identification problem
Automatica (Journal of IFAC)
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Blind identification using the kurtosis with applications to field data
Signal Processing
Stochastic realization with exogenous inputs and “subspace-methods” identification
Signal Processing - Special issue: subspace methods, part II: system identification
International Journal of Computer Vision
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Model (In)Validation Approach to Gait Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Bilinear Models for View-invariant Action and Identity Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes
International Journal of Computer Vision
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Further results and insights on subspace based sinusoidal frequencyestimation
IEEE Transactions on Signal Processing
Asymptotic properties of subspace estimators
Automatica (Journal of IFAC)
On the ill-conditioning of subspace identification with inputs
Automatica (Journal of IFAC)
An online incremental learning pattern-based reasoning system
Neural Networks
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Gait recognition based on improved dynamic Bayesian networks
Pattern Recognition
Brief paper: Consistency of subspace methods for signals with almost-periodic components
Automatica (Journal of IFAC)
Human action recognition by fast dense trajectories
Proceedings of the 21st ACM international conference on Multimedia
Fast action recognition using negative space features
Expert Systems with Applications: An International Journal
Journal of Visual Communication and Image Representation
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We address the problem of performing decision tasks, and in particular classification and recognition, in the space of dynamical models in order to compare time series of data. Motivated by the application of recognition of human motion in image sequences, we consider a class of models that include linear dynamics, both stable and marginally stable (periodic), both minimum and non-minimum phase, driven by non-Gaussian processes. This requires extending existing learning and system identification algorithms to handle periodic modes and nonminimum phase behavior, while taking into account higher-order statistics of the data. Once a model is identified, we define a kernel-based cord distance between models that includes their dynamics, their initial conditions as well as input distribution. This is made possible by a novel kernel defined between two arbitrary (non-Gaussian) distributions, which is computed by efficiently solving an optimal transport problem. We validate our choice of models, inference algorithm, and distance on the tasks of human motion synthesis (sample paths of the learned models), and recognition (nearest-neighbor classification in the computed distance). However, our work can be applied more broadly where one needs to compare historical data while taking into account periodic trends, non-minimum phase behavior, and non-Gaussian input distributions.