Teemu Roos

A Probabilistic Approach to WLAN User Location Estimation

We estimate the location of a WLAN user based on radio signal strength measurements performed by the users mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.

A statistical modeling approach to location estimation

Some location estimation methods, such as the GPS satellite navigation system, require nonstandard features either in the mobile terminal or the network. Solutions based on generic technologies not intended for location estimation purposes, such as the cell-ID method in GSM/GPRS cellular networks, are usually problematic due to their inadequate location estimation accuracy. In order to enable accurate location estimation when only inaccurate measurements are available, we present an approach to location estimation that is different from the prevailing geometric one. We call our approach the statistical modeling approach. As an example application of the proposed statistical modeling framework, we present a location estimation method based on a statistical signal power model. We also present encouraging empirical results from simulated experiments supported by real-world field tests.

On Discriminative Bayesian Network Classifiers and Logistic Regression

Discriminative learning of the parameters in the naive Bayes model is known to be equivalent to a logistic regression problem. Here we show that the same fact holds for much more general Bayesian network models, as long as the corresponding network structure satisfies a certain graph-theoretic property. The property holds for naive Bayes but also for more complex structures such as tree-augmented naive Bayes (TAN) as well as for mixed diagnostic-discriminative structures. Our results imply that for networks satisfying our property, the conditional likelihood cannot have local maxima so that the global maximum can be found by simple local optimization methods. We also show that if this property does not hold, then in general the conditional likelihood can have local, non-global maxima. We illustrate our theoretical results by empirical experiments with local optimization in a conditional naive Bayes model. Furthermore, we provide a heuristic strategy for pruning the number of parameters and relevant features in such models. For many data sets, we obtain good results with heavily pruned submodels containing many fewer parameters than the original naive Bayes model.

Topics in probabilistic location estimation in wireless networks

In this survey-style paper we demonstrate the usefulness of the probabilistic modelling framework in solving not only the actual positioning problem, but also many related problems involving issues like calibration, active learning, error estimation and tracking with history. We also point out some interesting links between positioning research done in the area of robotics and in the area of wireless radio networks.

Video: User-generated free-form gestures for authentication: security and memorability

This paper studies the security and memorability of free-form multitouch gestures for mobile authentication. Towards this end, we collected a dataset with a generate-test-retest paradigm where participants (N=63) generated free-form gestures, repeated them, and were later retested for memory. Half of the participants decided to generate one-finger gestures, and the other half generated multi-finger gestures. Although there has been recent work on template-based gestures, there are yet no metrics to analyze security of either template or free-form gestures. For example, entropy-based metrics used for text-based passwords are not suitable for capturing the security and memorability of free-form gestures. Hence, we modify a recently proposed metric for analyzing information capacity of continuous full-body movements for this purpose. Our metric computed estimated mutual information in repeated sets of gestures. Surprisingly, one-finger gestures had higher average mutual information. Gestures with many hard angles and turns had the highest mutual information. The best-remembered gestures included signatures and simple angular shapes. We also implemented a multitouch recognizer to evaluate the practicality of free-form gestures in a real authentication system and how they perform against shoulder surfing attacks. We discuss strategies for generating secure and memorable free-form gestures. We conclude that free-form gestures present a robust method for mobile authentication.

MDL Denoising Revisited

We refine and extend an earlier minimum description length (MDL) denoising criterion for wavelet-based denoising. We start by showing that the denoising problem can be reformulated as a clustering problem, where the goal is to obtain separate clusters for informative and noninformative wavelet coefficients, respectively. This suggests two refinements, adding a code-length for the model index, and extending the model in order to account for subband-dependent coefficient distributions. A third refinement is the derivation of soft thresholding inspired by predictive universal coding with weighted mixtures. We propose a practical method incorporating all three refinements, which is shown to achieve good performance and robustness in denoising both artificial and natural signals.

Conditional NML Universal Models

The NML (normalized maximum likelihood) universal model has certain minmax optimal properties but it has two shortcomings: the normalizing coefficient can be evaluated in a closed form only for special model classes, and it does not define a random process so that it cannot be used for prediction. We present a universal conditional NML model, which has minmax optimal properties similar to those of the regular NML model. However, unlike NML, the conditional NML model defines a random process which can be used for prediction. It also admits a recursive evaluation for data compression. The conditional normalizing coefficient is much easier to evaluate, for instance, for tree machines than the integral of the square root of the Fisher information in the NML model. For Bernoulli distributions, the conditional NML model gives a predictive probability, which behaves like the Krichevsky-Trofimov predictive probability, actually slightly better for extremely skewed strings. For some model classes, it agrees with the predictive probability found earlier by Takimoto and Warmuth, as the solution to a different more restrictive minmax problem. We also calculate the CNML models for the generalized Gaussian regression models, and in particular for the cases where the loss function is quadratic, and show that the CNML model achieves asymptotic optimality in terms of the mean ideal code length. Moreover, the quadratic loss, which represents fitting errors as noise rather than prediction errors, can be shown to be smaller than what can be achieved with the NML as well as with the so-called plug-in or the predictive MDL model.

Probabilistic Methods for Location Estimation in Wireless Networks

Probabilistic modeling techniques offer a unifying theoretical framework for solving the problems encountered when developing location-aware and location-sensitive applications in wireless radio networks. In this paper we demonstrate the usefulness of the probabilistic modelling framework in solving not only the actual location estimation (positioning) problem, but also many related problems involving pragmatically important issues like calibration, active learning, error estimation and tracking with history. Some interesting links between positioning research done in the area of robotics and in the area of wireless radio networks are also discussed.

Model selection by sequentially normalized least squares

Model selection by means of the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estimation. We introduce a new criterion based on sequentially minimized squared deviations, which are smaller than both the usual least squares and the squared prediction errors used in PLS. We also prove that our criterion has a probabilistic interpretation as a model which is asymptotically optimal within the given class of distributions by reaching the lower bound on the logarithmic prediction errors, given by the so called stochastic complexity, and approximated by BIC. This holds when the regressor (design) matrix is non-random or determined by the observed data as in AR models. The advantages of the criterion include the fact that it can be evaluated efficiently and exactly, without asymptotic approximations, and importantly, there are no adjustable hyper-parameters, which makes it applicable to both small and large amounts of data.

Learning locally minimax optimal Bayesian networks

We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background knowledge is available. The problem can be divided into two different subtasks: learning the structure of the network (a set of independence relations), and learning the parameters of the model (that fix the probability distribution from the set of all distributions consistent with the chosen structure). There are not many theoretical frameworks that consistently handle both these problems together, the Bayesian framework being an exception. In this paper we propose an alternative, information-theoretic framework which sidesteps some of the technical problems facing the Bayesian approach. The framework is based on the minimax optimal normalized maximum likelihood (NML) distribution, which is motivated by the minimum description length (MDL) principle. The resulting model selection criterion is consistent, and it provides a way to construct highly predictive Bayesian network models. Our empirical tests show that the proposed method compares favorably with alternative approaches in both model selection and prediction tasks.