Statistical Models for Co-occurrence Data

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets. The main challenge for statistical models in this context is to overcome the inherent data sparseness and to estimate the probabilities for pairs which were rarely observed or even unobserved in a given sample set. Moreover, it is often of considerable interest to extract grouping structure or to find a hierarchical data organization. A novel family of mixture models is proposed which explain the observed data by a finite number of shared aspects or clusters. This provides a common framework for statistical inference and structure discovery and also includes several recently proposed models as special cases. Adopting the maximum likelihood principle, EM algorithms are derived to fit the model parameters. We develop improved versions of EM which largely avoid overfitting problems and overcome the inherent locality of EM--based optimization. Among the broad variety of possible applications, e.g., in information retrieval, natural language processing, data mining, and computer vision, we have chosen document retrieval, the statistical analysis of noun/adjective co-occurrence and the unsupervised segmentation of textured images to test and evaluate the proposed algorithms.

New Architectural Models for Visibly Controllable Computing: The Relevance of Dynamic Object Oriented Architectures and Plan Based Computing Models

Traditionally, we've focussed on the question of how to make a system easy to code the first time, or perhaps on how to ease the system's continued evolution. But if we look at life cycle costs, then we must conclude that the important question is how to make a system easy to operate. To do this we need to make it easy for the operators to see what's going on and to then manipulate the system so that it does what it is supposed to. This is a radically different criterion for success. What makes a computer system visible and controllable? This is a difficult question, but it's clear that today's modern operating systems with nearly 50 million source lines of code are neither. Strikingly, the MIT Lisp Machine and its commercial successors provided almost the same functionality as today's mainstream sytsems, but with only 1 Million lines of code. This paper is a retrospective examination of the features of the Lisp Machine hardware and software system. Our key claim is that by building the Object Abstraction into the lowest tiers of the system, great synergy and clarity were obtained. It is our hope that this is a lesson that can impact tomorrow's designs. We also speculate on how the spirit of the Lisp Machine could be extended to include a comprehensive access control model and how new layers of abstraction could further enrich this model.

Learning Three-Dimensional Shape Models for Sketch Recognition

Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.