Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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25 Jun 2012
TL;DR: Evidence that channel responses from multiple OFDM subcarriers can be a promising location signature in WiFi systems is found, and PinLoc, a functional system implemented on off-the-shelf Intel 5300 cards, is evaluated.
Abstract: This paper explores the viability of precise indoor localization using physical layer information in WiFi systems. We find evidence that channel responses from multiple OFDM subcarriers can be a promising location signature. While these signatures certainly vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We attempt to harness these opportunities through a functional system called PinLoc, implemented on off-the-shelf Intel 5300 cards. We evaluate the system in a busy engineering building, a crowded student center, a cafeteria, and at the Duke University museum, and demonstrate localization accuracies in the granularity of 1m x 1m boxes, called "spots". Results from 100 spots show that PinLoc is able to localize users to the correct spot with 89% mean accuracy, while incurring less than 6% false positives. We believe this is an important step forward, compared to the best indoor localization schemes of today, such as Horus.
504 citations
Cites background or methods from "Pattern Recognition and Machine Lea..."
...The classical approach to estimate these parameters is the expectation-maximization algorithm [16]....
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...Instead, we estimate the parameters using variational Bayesian inference [16]....
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TL;DR: This paper is the first validated application of real-time cortical connectivity analysis and cognitive state classification from highdensity wearable dry EEG to 64-channel dry EEG, addressing a need for robust real- time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting.
Abstract: Goal: We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. Methods: The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. Results: Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA $(0.74 \pm 0.09)$ and LCMV $(0.72 \pm 0.08)$ source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA $(0.74 \pm 0.16)$ but significantly better for LCMV $(0.82 \pm 0.12)$ . Conclusion: We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. Significance: This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain–computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.
503 citations
TL;DR: This paper discusses how domain knowledge influences design of the Gaussian process models and provides case examples to highlight the approaches.
Abstract: In this paper, we offer a gentle introduction to Gaussian processes for time-series data analysis. The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes . We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the approaches.
502 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...The position of a particular changepoint becomes a hyperparameter of the model that is marginalized using Bayesian quadrature....
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...Osborne M, Reece S, Rogers A, Roberts S, Garnett R. 2010 Sequential Bayesian prediction in the presence of changepoints and faults....
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...As a framework for reasoning in the presence of uncertain, incomplete and delayed information, we appeal to Bayesian inference....
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...The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes....
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...Rasmussen CE, Ghahramani Z. 2003 Bayesian Monte Carlo....
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TL;DR: An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced, consisting of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees.
Abstract: An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees. Such an algorithm generalizes and improves existing density-based clustering techniques with respect to different aspects. It provides as a result a complete clustering hierarchy composed of all possible density-based clusters following the nonparametric model adopted, for an infinite range of density thresholds. The resulting hierarchy can be easily processed so as to provide multiple ways for data visualization and exploration. It can also be further postprocessed so that: (i) a normalized score of “outlierness” can be assigned to each data object, which unifies both the global and local perspectives of outliers into a single definition; and (ii) a “flat” (i.e., nonhierarchical) clustering solution composed of clusters extracted from local cuts through the cluster tree (possibly corresponding to different density thresholds) can be obtained, either in an unsupervised or in a semisupervised way. In the unsupervised scenario, the algorithm corresponding to this postprocessing module provides a global, optimal solution to the formal problem of maximizing the overall stability of the extracted clusters. If partially labeled objects or instance-level constraints are provided by the user, the algorithm can solve the problem by considering both constraints violations/satisfactions and cluster stability criteria. An asymptotic complexity analysis, both in terms of running time and memory space, is described. Experiments are reported that involve a variety of synthetic and real datasets, including comparisons with state-of-the-art, density-based clustering and (global and local) outlier detection methods.
500 citations
Cites background from "Pattern Recognition and Machine Lea..."
...The construction of an estimate of such a PDF from the observed data is a problem of particular relevance, for example, for analyzing and understanding the corresponding generating mechanism(s)....
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TL;DR: This application of Bayes' Theorem automatically applies a quantitative Ockham's razor that penalizes the data‐fit of more complex model classes that extract more information from the data.
Abstract: Probability logic with Bayesian updating provides a rigorous framework to quantify modeling uncertainty and perform system identification. It uses probability as a multi-valued propositional logic for plausible reasoning where the probability of a model is a measure of its relative plausibility within a set of models. System identification is thus viewed as inference about plausible system models and not as a quixotic quest for the true model. Instead of using system data to estimate the model parameters, Bayes' Theorem is used to update the relative plausibility of each model in a model class, which is a set of input–output probability models for the system and a probability distribution over this set that expresses the initial plausibility of each model. Robust predictive analyses informed by the system data use the entire model class with the probabilistic predictions of each model being weighed by its posterior probability. Additional robustness to modeling uncertainty comes from combining the robust predictions of each model class in a set of candidates for the system, where each contribution is weighed by the posterior probability of the model class. This application of Bayes' Theorem automatically applies a quantitative Ockham's razor that penalizes the data-fit of more complex model classes that extract more information from the data. Robust analyses involve integrals over parameter spaces that usually must be evaluated numerically by Laplace's method of asymptotic approximation or by Markov Chain Monte Carlo methods. An illustrative application is given using synthetic data corresponding to a structural health monitoring benchmark structure.
497 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...where c 1⁄4 pðDjMÞ 1⁄4 R Y pðDjh;MÞpðhjMÞdh is the normalizing constant; pðDjh;MÞ as a function of h is the likelihood function, which expresses the probability of getting data D based on the PDF pðxju;h;MÞ for the system output given by the model class M; and pðhjMÞ is the prior PDF specified by M which is chosen to quantify the initial plausibility of each model defined by the value of the parameter vector h; for example, it can be chosen to provide regularization of ill-conditioned inverse problems [17,18]....
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