Desirable Characteristics of a model include good representation of objects, fast and efficient learning algorithms that require as little supervised information as possible.

We describe a hierarchical compositional system for detecting deformable objects in images. Objects are represented by graphical models. The algorithm uses a hierarchical tree where the root of the tree corresponds to the full object and lower-level elements of the tree correspond to simpler features.

While kernel canonical correlation analysis (kernel CCA) has been applied in many problems, the asymptotic convergence of the functions estimatedfrom a finite sample to the true functions has not yet been established. This paper gives a rigorous proof of the statistical convergenceof kernel CCA and a related method (NOCCO), which provides a theoretical justification for these methods. The result also gives a sufficient condition on the decay of the regularization coefficientin the methods to ensure convergence.

We consider regularized least-squares (RLS) with a Gaussian kernel.

Although variants of value iteration have been proposed for finding Nash or correlated equilibria in general-sum Markov games, these variants have not been shown to be effective in general. In this paper, we demonstrate byconstruction that existing variants of value iteration cannot find stationary equilibrium policies in arbitrary general-sum Markov games. Instead, we propose an alternative interpretation of the output of value iteration basedon a new (non-stationary) equilibrium concept that we call "cyclic equilibria." We prove that value iteration identifies cyclic equilibria ina class of games in which it fails to find stationary equilibria. We also demonstrate empirically that value iteration finds cyclic equilibria in nearly all examples drawn from a random distribution of Markov games.

Clustering is a fundamental problem in machine learning and has been approached in many ways. Two general and quite different approaches include iteratively fitting a mixture model (e.g., using EM) and linking together pairs of training cases that have high affinity (e.g., using spectral methods). Pairwise clustering algorithms need not compute sufficient statistics and avoid poor solutions by directly placing similar examples in the same cluster. However, many applications require that each cluster of data be accurately described by a prototype or model, so affinity-based clustering - and its benefits - cannot be directly realized. We describe a technique called "affinity propagation", which combines the advantages of both approaches. The method learns a mixture model of the data by recursively propagating affinity messages. We demonstrate affinity propagation on the problems of clustering image patches for image segmentation and learning mixtures of gene expression models from microarray data. We find that affinity propagation obtains better solutions than mixtures of Gaussians, the K-medoids algorithm, spectral clustering and hierarchical clustering, and is both able to find a pre-specified number of clusters and is able to automatically determine the number of clusters. Interestingly, affinity propagation can be viewed as belief propagation in a graphical model that accounts for pairwise training case likelihood functions and the identification of cluster centers.

A 5-layer neuromorphic vision processor whose components communicate spike events asychronously using the address-eventrepresentation (AER) is demonstrated. The system includes a retina chip, two convolution chips, a 2D winner-take-all chip, a delay line chip, a learning classifier chip, and a set of PCBs for computer interfacing and address space remappings. The components use a mixture of analog and digital computation and will learn to classify trajectories of a moving object. A complete experimental setup and measurements results are shown.

This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.

Fisher linear discriminant analysis (LDA) can be sensitive to the problem data.

A dynamic texture is a video model that treats a video as a sample from a spatiotemporal stochastic process, specifically a linear dynamical system. Oneproblem associated with the dynamic texture is that it cannot model video where there are multiple regions of distinct motion. In this work, we introduce the layered dynamic texture model, which addresses this problem. We also introduce a variant of the model, and present the EM algorithm for learning each of the models. Finally, we demonstrate the efficacy of the proposed model for the tasks of segmentation and synthesis ofvideo.