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.

Psychologically targeted persuasive advertising and product information in e-commerce

Abstract: In this paper, we describe a framework for a personalization system to systematically induce desired emotion and attention related states and promote information processing in viewers of online advertising and e-commerce product information. Psychological Customization entails personalization of the way of presenting information (user interface, visual layouts, modalities, structures) per user to create desired transient psychological effects and states, such as emotion, attention, involvement, presence, persuasion and learning. Conceptual foundations and empiric evidence for the approach are presented.

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.

Minimum-link paths revisited

Abstract: A path or a polygonal domain is C-oriented if the orientations of its edges belong to a set of C given orientations; this is a generalization of the notable rectilinear case ( C = 2 ). We study exact and approximation algorithms for minimum-link C-oriented paths and paths with unrestricted orientations, both in C-oriented and in general domains.Our two main algorithms are as follows:A subquadratic-time algorithm with a non-trivial approximation guarantee for general (unrestricted-orientation) minimum-link paths in general domains.An algorithm to find a minimum-link C-oriented path in a C-oriented domain. Our algorithm is simpler and more time-space efficient than the prior algorithm.We also obtain several related results:3SUM-hardness of determining the link distance with unrestricted orientations (even in a rectilinear domain).An optimal algorithm for finding a minimum-link rectilinear path in a rectilinear domain. The algorithm and its analysis are simpler than the existing ones.An extension of our methods to find a C-oriented minimum-link path in a general (not necessarily C-oriented) domain.A more efficient algorithm to compute a 2-approximate C-oriented minimum-link path.A notion of "robust" paths. We show how minimum-link C-oriented paths approximate the robust paths with unrestricted orientations to within an additive error of 1.

Bayesian Multi-view Tensor Factorization

Abstract: We introduce a Bayesian extension of the tensor factorization problem to multiple coupled tensors. For a single tensor it reduces to standard PARAFAC-type Bayesian factorization, and for two tensors it is the first Bayesian Tensor Canonical Correlation Analysis method. It can also be seen to solve a tensorial extension of the recent Group Factor Analysis problem. The method decomposes the set of tensors to factors shared by subsets of the tensors, and factors private to individual tensors, and does not assume orthogonality. For a single tensor, the method empirically outperforms existing methods, and we demonstrate its performance on multiple tensor factorization tasks in toxicogenomics and functional neuroimaging.

Optimization Equivalence of Divergences Improves Neighbor Embedding

Abstract: Visualization methods that arrange data objects in 2D or 3D layouts have followed two main schools, methods oriented for graph layout and methods oriented for vectorial embedding. We show the two previously separate approaches are tied by an optimization equivalence, making it possible to relate methods from the two approaches and to build new methods that take the best of both worlds. In detail, we prove a theorem of optimization equivalences between β- and γ-, as well as α- and Renyi-divergences through a connection scalar. Through the equivalences we represent several nonlinear dimensionality reduction and graph drawing methods in a generalized stochastic neighbor embedding setting, where information divergences are minimized between similarities in input and output spaces, and the optimal connection scalar provides a natural choice for the tradeoff between attractive and repulsive forces. We give two examples of developing new visualization methods through the equivalences: 1) We develop weighted symmetric stochastic neighbor embedding (ws-SNE) from Elastic Embedding and analyze its benefits, good performance for both vectorial and network data; in experiments ws-SNE has good performance across data sets of different types, whereas comparison methods fail for some of the data sets; 2) we develop a γ-divergence version of a PolyLog layout method; the new method is scale invariant in the output space and makes it possible to efficiently use large-scale smoothed neighborhoods.