The information bottleneck method is an unsupervised model independent data organization technique. Given a joint distribution peA, B), this method constructs anew variable T that extracts partitions, or clusters, over the values of A that are informative about B. In a recent paper, we introduced a general principled frameworkfor multivariate extensions of the information bottleneck method that allows us to consider multiple systems of data partitions that are interrelated. In this paper, we present a new family of simple agglomerative algorithms to construct such systems of interrelated clusters. We analyze the behavior of these algorithms and apply them to several real-life datasets.

A nonlinear supervised learning model, the Specialized Mappings Architecture (SMA), is described and applied to the estimation of human body pose from monocular images. The SMA consists of several specialized forward mapping functions and an inverse mapping function.Each specialized function maps certain domains of the input space (image features) onto the output space (body pose parameters). The key algorithmic problems faced are those of learning the specialized domains and mapping functions in an optimal way,as well as performing inference given inputs and knowledge of the inverse function. Solutions to these problems employ the EM algorithm and alternating choices of conditional independence assumptions.Performance of the approach is evaluated with synthetic and real video sequences of human motion. 1 Introduction In everyday life, humans can easily estimate body part locations (body pose) from relatively low-resolution images of the projected 3D world (e.g., when viewing a photograph or a video). However, body pose estimation is a very difficult computer vision problem.

This paper describes a clustering algorithm for vector quantizers using a "stochastic association model". It offers a new simple and powerful softmax adaptationrule. The adaptation process is the same as the online K-means clustering method except for adding random fluctuation in the distortion error evaluation process. Simulation results demonstrate that the new algorithm can achieve efficient adaptation as high as the "neural gas" algorithm, which is reported as one of the most efficient clustering methods. It is a key to add uncorrelated random fluctuation in the similarity evaluationprocess for each reference vector. For hardware implementation ofthis process, we propose a nanostructure, whose operation is described by a single-electron circuit. It positively uses fluctuation in quantum mechanical tunneling processes.

Driven by the progress in the field of single-trial analysis of EEG, there is a growing interest in brain computer interfaces (BCIs), i.e., systems that enable human subjects to control a computer only by means of their brain signals. In a pseudo-online simulation our BCI detects upcoming finger movements in a natural keyboard typing condition and predicts their laterality. Thiscan be done on average 100-230 ms before the respective key is actually pressed, i.e., long before the onset of EMG. Our approach is appealing for its short response time and high classification accuracy ( 96%) in a binary decision where no human training is involved. We compare discriminative classifiers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable regularization propertiesfor dealing with high noise cases (inter-trial variablity).

An important issue in applying SVMs to speech recognition is the ability to classify variable length sequences. This paper presents extensions to a standard scheme for handling this variable length data, the Fisher score. A more useful mapping is introduced based on the likelihood-ratio. The score-space defined by this mapping avoids some limitations of the Fisher score. Class-conditional generative modelsare directly incorporated into the definition of the score-space.

First, there are a wide variety of algorithms that use the eigenvectors in slightly different ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems.

In this paper we introduce a new sparseness inducing prior which does not involve any (hyper)parameters thatneed to be adjusted or estimated. Although other applications are possible, we focus here on supervised learning problems: regression and classification. Experiments withseveral publicly available benchmark data sets show that the proposed approach yields state-of-the-art performance. In particular, our method outperforms support vector machines and performs competitively with the best alternative techniques, both in terms of error rates and sparseness, although it involves no tuning or adjusting of sparsenesscontrolling hyper-parameters.

We propose the following general method for scaling learning algorithms to arbitrarily large data sets. We apply this method to the EM algorithm for mixtures of Gaussians. Preliminary experiments on a series of large data sets provide evidence of the potential of this approach. On the other hand, they require large computational resources to learn from. While in the past the factor limiting the quality of learnable models was typically the quantity of data available, in many domains today data is superabundant, and the bottleneck is t he time required to process it.

This paper presents a novel approach to the unsupervised learning of syntactic analysesof natural language text.

SMC is often referred to as particle filtering (PF) in the context of computing filtering distributions for statistical inference and learning. It is known that the performance of PF often deteriorates in high-dimensional state spaces. In the past, we have shown that if a model admits partial analytical tractability, it is possible to combine PF with exact algorithms (Kalman filters, HMM filters, junction tree algorithm) to obtain efficient high dimensional filters (Doucet, de Freitas, Murphy and Russell 2000, Doucet, Godsill and Andrieu 2000). In particular, we exploited a marginalisation technique known as Rao-Blackwellisation (RB). Here, we attack a more complex model that does not admit immediate analytical tractability. This probabilistic model consists of Gaussian latent variables and binary observations.We show that by augmenting the model with artificial variables, it becomes possible to apply Rao-Blackwellisation and optimal sampling strategies. We focus on the problem of sequential binary classification (that is, when the data arrives one-at-a-time) using generic classifiers that consist of linear combinations of basis functions, whose coefficients evolve according to a Gaussian smoothness prior (Kitagawa and Gersch 1996). We have previously addressed this problem in the context of sequential fault detection in marine diesel engines (H0jen-S0rensen, de Freitas and Fog 2000). This application is of great importance as early detection of incipient faults can improve safety and efficiency, as well as, help to reduce downtime andplant maintenance in many industrial and transportation environments.