35531897
Decomposition methods for convolution operators
This paper presents methods for the decomposition of two-dimensional convolution operators of size m × n into sums and products of 3 × 3 operators. If m = n 2k + 1, then the maximum number of 3 × 3 operators needed in the decomposition is k2 + k − 1. In particular, every 5 × 5 operator can be decomposed into the sum and product of at most five 3 × 3 operators. A characterization is given for 5 × 5 operators which can be decomposed into the sum and product of at most three 3 × 3 operators. With operators exhibiting symmetry with respect to both axes and a diagonal, the maximum number of operators needed is k(k + 1)2.
9645862
Aq-Series Approach to Deblurring the Discrete Gaussian
A method is presented for deblurring an image blurred by the discrete Gaussian. The method, based on classical theorems of Jacobi and Ramanujan, not only provides exact formulas for the deblurring, but also condition numbers and error bounds estimating the agreement between the original and reconstructed image. The use of the Jacobi Triple Product Theorem provides a convenient factorization of the formulas used in the inversion process into a product of three infinite series. These three series correspond to a constant together with a Toeplitz operator and its transpose. In the finite setting this factorization corresponds to the factorization of a matrix into the product of Toeplitz matrices, where the entries can be computed using simple recursion formulas. For selected choices of ?, condition numbers are calculated for these operators. The results are similar to a method developed by Kimia and Zucker.
13984336
Improving the Quality of the Personalized Electronic Program Guide
As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system.
1695456
Case-Based Similarity Assessment: Estimating Adaptability from Experience
Case-based problem-solving systems rely on similarity assessment to select stored cases whose solutions are easily adaptable to fit current problems. However, widely-used similarity assessment strategies, such as evaluation of semantic similarity, can be poor predictors of adaptability. As a result, systems may select cases that are difficult or impossible for them to adapt, even when easily adaptable cases are available in memory. This paper presents a new similarity assessment approach which couples similarity judgments directly to a case library containing the system's adaptation knowledge. It examines this approach in the context of a case-based planning system that learns both new plans and new adaptations. Empirical tests of alternative similarity assessment strategies show that this approach enables better case selection and increases the benefits accrued from learned adaptations.
15894358
Using Collaborative Filtering Data in Case-Based Recommendation
In the context of PTV, an applied recommender system operating in the TV listings domain, we are examining the potential benefits in merging case-based and collaborative filtering (CF) recommendation techniques by developing case-based reasoning (CBR) methods that employ collaborative filtering style ratings profiles directly as cases. Doing so presents a number of challenges, both in applying a case-based perspective to collaborative filtering, and in addressing the sparsity problem that plagues many collaborative filtering systems. This paper expands on earlier CBR views of collaborative filtering, identifies problems and opportunities for similarity maintenance therein, and proposes and evaluates methods for mining and applying new similarity knowledge.
29970366
Multimodal Interaction - Improving Usability and Efficiency in a Mobile GIS Context
The context of mobility raises many issues for GIS applications. Mobile device limitations, including pen input whilst in motion, result in interfaces which are difficult to navigate and interact with. However, comparatively little research has been conducted to address the interface mobility problem for GIS. We are particularly concerned with the limited interaction techniques available to users of mobile GIS which play a primary role in contributing to the complexity of using such an application whilst mobile. Our research focuses on multimodal interfaces as a means to present users with a wider choice of modalities for interacting with GIS applications. The focus of this paper concerns a comprehensive user study which demonstrates the benefits, in terms of usability and efficiency, of a multimodal interface for the CoMPASS mobile GIS which we have developed.
2193353
Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning
The case based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use for each individual knowledge source, but also how the different learning mechanisms interact and their combined utility. This paper presents a case study examining the relative contributions and costs involved in learning processes for three different knowledge sources--cases, case adaptation knowledge, and similarity information--in a casebased planner. It demonstrates the importance of interactions between different learning processes and identifies a promising method for integrating multiple learning methods to improve case-based reasoning.