Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval

Abstract: Estimating the degree of similarity between images is a challenging task as the similarity always depends on the context. Because of this context dependency, it seems quite impossible to create a universal metric for the task. The number of low-level features on which the judgement of similarity is based may be rather low, however. One approach to quantifying the similarity of images is to estimate the (joint) complexity of images based on these features. We present a novel method to estimate the complexity of images, based on ICA. We further use this to model joint complexity of images, which gives distances that can be used in content-based retrieval. We compare this new method to two other methods, namely estimating mutual information of images using marginal Kullback-Leibler divergence and approximating the Kolmogorov complexity of images using Normalized Compression Distance.

Optimizing content-based routers: posets and forests

Abstract: Event-based systems are seen as good candidates for supporting distributed applications in dynamic and ubiquitous environments because they support decoupled and asynchronous one-to-many and many-to-many information dissemination. Event systems are widely used because asynchronous messaging provides a flexible alternative to RPC. They are typically implemented using an overlay network of routers. A content-based router forwards event messages based on filters that are installed by subscribers and other routers. This paper addresses the optimization of content-based routing tables organized using the covering relation and presents novel configurations for improving local and distributed operation. We present the poset-derived forest data structure and variants that perform considerably better under frequent filter additions and removals than existing data structures. The results offer a significant performance increase to currently known covering-based routing mechanisms.

Local structure discovery in Bayesian networks

Abstract: Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning. In local learning, the modeler has one or more target variables that are of special interest; he wants to learn the structure near the target variables and is not interested in the rest of the variables. In this paper, we present a score-based local learning algorithm called SLL. We conjecture that our algorithm is theoretically sound in the sense that it is optimal in the limit of large sample size. Empirical results suggest that SLL is competitive when compared to the constraint-based HITON algorithm. We also study the prospects of constructing the network structure for the whole node set based on local results by presenting two algorithms and comparing them to several heuristics.