Discriminative Density-ratio Estimation

The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on developing Density-ratio (DR) estimation techniques that apply to both regression and classification problems. Although these methods work well for regression problems, their performance on classification problems is not satisfactory. This is due to a key observation that these methods focus on matching the sample marginal distributions without paying attention to preserving the separation between classes in the reweighted space. In this paper, we propose a novel method for Discriminative Density-ratio (DDR) estimation that addresses the aforementioned problem and aims at estimating the density-ratio of joint distributions in a class-wise manner. The proposed algorithm is an iterative procedure that alternates between estimating the class information for the test data and estimating new density ratio for each class. To incorporate the estimated class information of the test data, a soft matching technique is proposed. In addition, we employ an effective criterion which adopts mutual information as an indicator to stop the iterative procedure while resulting in a decision boundary that lies in a sparse region. Experiments on synthetic and benchmark datasets demonstrate the superiority of the proposed method in terms of both accuracy and robustness

A Study of Mobility Models in Mobile Surveillance Systems

This thesis explores the role mobile sensor's mobility model and how it affects surveillance system performance in term of area coverage and detection effectiveness. Several algorithms which are categorized into three types, namely, fully coordinated mobility, fully random mobility and emergent mobility models are discussed with their advantages and limitations. A multi-agent platform to organize mobile sensor nodes, control nodes and actor nodes was implemented. It demonstrated great flexibility and was favourable for its distributed, autonomous and cooperative problem-solving characters. Realistic scenarios which are based on three KheperaIII mobile robots and a model which mimics Waterloo regional airport were used to examine the implementation platform and evaluate performance of different mobility algorithms. Several practical issues related to software configurations and interface library were addressed as by-products. The experimental results from both simulation and real platform show that the area coverage and the detection effectiveness vary with applying different mobility models. Fully coordinated model's super efficiency comes with carefully task planning and high requirements of sensor navigational accuracy. Fully random model is the least efficient in area coverage and detection because of the repetitive searching of each sensor and among sensors. A self-organizing algorithm named anti-flocking which mimics solitary animal's social behaviour was first proposed. It works based on quite simple rules for achieving purposeful coordinated group action without explicit global control. Experimental results demonstrate its attractive target detection efficiency in term of both detection rate and detection time while providing desirable features such as scalability, robustness and adaptivity. From the simulation results, the detection rate of the anti-flocking model increases by 36.5% and average detection time decreases by 46.2% comparing with the fully random motion model. The real platform results also reflect the superior performance improvement

Adaptive Learning Algorithms for Non-stationary Data

With the wide availability of large amounts of data and acute need for extracting useful information from such data, intelligent data analysis has attracted great attention and contributed to solving many practical tasks, ranging from scientific research, industrial process and daily life. In many cases the data evolve over time or change from one domain to another. The non-stationary nature of the data brings a new challenge for many existing learning algorithms, which are based on the stationary assumption. This dissertation addresses three crucial problems towards the effective handling of non-stationary data by investigating systematic methods for sample reweighting. Sample reweighting is a problem that infers sample-dependent weights for a data collection to match another data collection which exhibits distributional difference. It is known as the density-ratio estimation problem and the estimation results can be used in several machine learning tasks. This research proposes a set of methods for distribution matching by developing novel density-ratio methods that incorporate the characters of different non-stationary data analysis tasks. The contributions are summarized below. First, for the domain adaptation of classification problems a novel discriminative density-ratio method is proposed. This approach combines three learning objectives: minimizing generalized risk on the reweighted training data, minimizing class-wise distribution discrepancy and maximizing the separation margin on the test data. To solve the discriminative density-ratio problem, two algorithms are presented on the basis of a block coordinate update optimization scheme. Experiments conducted on different domain adaptation scenarios demonstrate the effectiveness of the proposed algorithms. Second, for detecting novel instances in the test data a locally-adaptive kernel density-ratio method is proposed. While traditional novelty detection algorithms are limited to detect either emerging novel instances which are completely new, or evolving novel instances whose distribution are different from previously-seen ones, the proposed algorithm builds on the success of the idea of using density ratio as a measure of evolving novelty and augments with structural information of each data instance's neighborhood. This makes the estimation of density ratio more reliable, and results in detection of emerging as well as evolving novelties. In addition, the proposed locally-adaptive kernel novelty detection method is applied in the social media analysis and shows favorable performance over other existing approaches. As the time continuity of social media streams, the novelty is usually characterized by the combination of emerging and evolving. One reason is the existence of large common vocabularies between different topics. Another reason is that there are high possibilities of topics being continuously discussed in sequential batch of collections, but showing different level of intensity. Thus, the presented novelty detection algorithm demonstrates its effectiveness in the social media data analysis. Lastly, an auto-tuning method for the non-parametric kernel mean matching estimator is presented. It introduces a new quality measure for evaluating the goodness of distribution matching which reflects the normalized mean square error of estimates. The proposed quality measure does not depend on the learner in the following step and accordingly allows the model selection procedures for importance estimation and prediction model learning to be completely separated

Search for Kaluza-Klein Graviton Emission in ppˉp\bar{p} Collisions at s=1.8\sqrt{s}=1.8 TeV using the Missing Energy Signature

We report on a search for direct Kaluza-Klein graviton production in a data sample of 84 ${pb}^{-1}$ of \ppb collisions at $\sqrt{s}$ = 1.8 TeV, recorded by the Collider Detector at Fermilab. We investigate the final state of large missing transverse energy and one or two high energy jets. We compare the data with the predictions from a $3+1+n$-dimensional Kaluza-Klein scenario in which gravity becomes strong at the TeV scale. At 95% confidence level (C.L.) for $n$=2, 4, and 6 we exclude an effective Planck scale below 1.0, 0.77, and 0.71 TeV, respectively.Comment: Submitted to PRL, 7 pages 4 figures/Revision includes 5 figure

Measurement of the average time-integrated mixing probability of b-flavored hadrons produced at the Tevatron

We have measured the number of like-sign (LS) and opposite-sign (OS) lepton pairs arising from double semileptonic decays of $b$ and $\bar{b}$-hadrons, pair-produced at the Fermilab Tevatron collider. The data samples were collected with the Collider Detector at Fermilab (CDF) during the 1992-1995 collider run by triggering on the existence of $\mu \mu$ and $e \mu$ candidates in an event. The observed ratio of LS to OS dileptons leads to a measurement of the average time-integrated mixing probability of all produced $b$-flavored hadrons which decay weakly, $\bar{\chi} = 0.152 \pm 0.007$ (stat.) $\pm 0.011$ (syst.), that is significantly larger than the world average $\bar{\chi} = 0.118 \pm 0.005$.Comment: 47 pages, 10 figures, 15 tables Submitted to Phys. Rev.

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012