D4L: Decentralized dynamic discriminative dictionary learning

We consider discriminative dictionary learning in a distributed online setting, where a team of networked robots aims to jointly learn both a common basis of the feature space and a classifier over this basis from sequentially observed signals. We formulate this problem as a distributed stochastic program with a non-convex objective and present a block variant of the Arrow-Hurwicz saddle point algorithm to solve it. Only neighboring nodes in the communications network need to exchange information, and we penalize the discrepency between the individual feature basis and classifiers using Lagrange multipliers. The application we consider is for a team of robots to collaboratively recognize objects of interest in dynamic environments. As a preliminary performance benchmark, we consider the problem of learning a texture classifier across a network of robots moving around an urban setting where separate training examples are sequentially observed at each robot. Results are shown for both a standard texture dataset and a new dataset from an urban training facility, and we compare the performance of the standard centralized construction to the new distributed algorithm for the case when distinct samples from all classes are seen by the robots. These experiments yield comparable performance between the decentralized and the centralized cases, demonstrating the proposed method's practical utility.

Authors

• Alec Koppel
• Garrett Warnell
• Ethan Stump
• Alejandro Ribeiro

Keywords

• Dictionaries
• Stochastic processes
• Encoding
• Robots
• Optimization
• Heuristic algorithms
• Training
• Discriminative Learning
• Dictionary Learning
• Performance Comparison
• Dynamic Environment
• Lagrange Multiplier
• Saddle Point
• Training Examples
• Distributed Algorithm
• Swarm Robotics
• Robotic Network
• Loss Function
• Model Parameters
• Optimization Problem
• Singular Value
• Object Recognition
• Stochastic Gradient
• Model Discrimination
• Stochastic Optimization
• Penalty Term
• Sparse Representation
• Sparse Coding
• Texture Classification
• Stochastic Algorithm
• Signal Representation
• Stochastic Approximation
• Dual Domain
• Steepest Descent Method
• Realizations Of Random Variables
• Stochastic Optimization Problem
• Numerical Stability