Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system.

For model adaptation of fully connected neural network layers, we provide an information geometric and sample behavioral active learning uncertainty sampling objective analysis. We identify conditions under which several uncertainty-based methods have the same performance and show that such conditions are more likely to appear in the early stages of learning. We define riskier samples for adaptation, and demonstrate that, as the set of labeled samples increases, margin-based sampling outperforms other uncertainty sampling methods by preferentially selecting these risky samples. We support our derivations and illustrations with experiments using Meta-Dataset, a benchmark for few-shot learning. We compare uncertainty-based active learning objectives using features produced by SimpleCNAPS (a state-of-the-art few-shot classifier) as input for a fully-connected adaptation layer. Our results indicate that margin-based uncertainty sampling achieves similar performance as other uncertainty based sampling methods with fewer labelled samples as discussed in the novel geometric analysis.

Cognitive radar framework has recently been proposed in radar signal processing to develope algorithms for target detection, tracking, and waveform design in the presence of nonstationary environmental (clutter) characteristics. In this framework, there are the three main steps: sensing the environmental changes, learning the new environmental statistical characteristics, and adapting the radar algorithms to the new characteristics. Here, we focus on the second step of the framework to identify the new clutter characteristics after a change is detected in the environment. We form a dictionary of various clutter distributions and identify the distribution of the new clutter data through matching pursuit using probabilistic similarity and distance measures under sparsity constraints. Specifically, we use inner-product as a similarity measure, and we apply three different L 1 -norm type probabilistic distance measures. We both numerically and analytically analyze their clutter-distribution identification performances and show that Kulczynski is the best distance measure for distribution identification.

In this paper, we introduce a transfer learning approach for our novel hybrid brain-computer interface in which electroencephalography and functional transcranial Doppler ultrasound are used simultaneously to record brain electrical activity and cerebral blood velocity respectively due to flickering mental rotation and word generation tasks. We reduced each trial into a scalar score using Regularized Discriminant Analysis (RDA). For each individual, class conditional probabilistic distribution of each mental task was estimated using RDA scores of the trials corresponding to that mental task. Similarities between class conditional distributions across individuals were measured using Kullback-Leibler divergence, Bhattacharyya, and Hellinger distances. Classification task was performed using Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM). We demonstrate that transfer learning can reduce calibration requirements up to %87.5. Moreover, it was found that QDA provides the most significant performance improvement compared to the case when no transfer learning is employed.

In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost. In this paper, we (a) propose a criterion based on a linear combination of state posterior and its changes, (b) show that for discrete-valued state estimation scenarios the proposed objective is more likely to sort correct and incorrect estimates appropriately compared to just looking at the posterior, and finally (c) demonstrate that the method can lead to significant human intent estimation speed increase without significant loss of accuracy in a brain-computer interface application.

The task of labeling samples is demanding and expensive. Active learning aims to generate the smallest possible training data set that results in a classifier with high performance in the test phase. It usually consists of two steps of selecting a set of queries and requesting their labels. Among the suggested objectives to score the query sets, information theoretic measures have become very popular. Yet among them, those based on Fisher information (FI) have the advantage of considering the diversity among the queries and tractable computations. In this work, we provide a practical algorithm based on Fisher information ratio to obtain query distribution for a general framework where, in contrast to the previous FI-based querying methods, we make no assumptions over the test distribution. The empirical results on synthetic and real-world data sets indicate that this algorithm gives competitive results.

In stochastic linear/non-linear active dynamic systems, states are estimated with the evidence through recursive measurements in response to queries of the system about the state to be estimated. Therefore, query selection is essential for such systems to improve state estimation accuracy and time. Query selection is conventionally achieved by minimization of the evidence variance or optimization of various information theoretic objectives. It was shown that optimization of mutual information-based objectives and variance-based objectives arrive at the same solution. However, existing approaches optimize approximations to the intended objectives rather than solving the exact optimization problems. To overcome these shortcomings, we propose an active querying procedure using mutual information maximization in recursive state estimation. First we show that mutual information generalizes variance based query selection methods and show the equivalence between objectives if the evidence likelihoods have unimodal distributions. We then solve the exact optimization problem for query selection and propose a query (measurement) selection algorithm. We specifically formulate the mutual information maximization for query selection as a combinatorial optimization problem and show that the objective is sub-modular, therefore can be solved efficiently with guaranteed convergence bounds through a greedy approach. Additionally, we analyze the performance of the query selection algorithm by testing it through a brain computer interface typing system.

Query selection for latent variable estimation is conventionally performed by opting for observations with low noise or optimizing information theoretic objectives related to reducing the level of estimated uncertainty based on the current best estimate. In these approaches, typically the system makes a decision by leveraging the current available information about the state. However, trusting the current best estimate results in poor query selection when truth is far from the current estimate, and this negatively impacts the speed and accuracy of the latent variable estimation procedure. We introduce a novel sequential adaptive action value function for query selection using the multi-armed bandit (MAB) framework which allows us to find a tractable solution. For this adaptive-sequential query selection method, we analytically show: (i) performance improvement in the query selection for a dynamical system, (ii) the conditions where the model outperforms competitors. We also present favorable empirical assessments of the performance for this method, compared to alternative methods, both using Monte Carlo simulations and human-in-the-loop experiments with a brain computer interface (BCI) typing system where the language model provides the prior information.

All modern emotion theoretical views assume a role for peripheral physiological changes during emotional experiences. In this paper, we explored the correlation between autonomically-mediated changes in multimodal bodily signals and discrete emotional states. In order to fully exploit the information in each modality, week learners based on individual signal modalities are built and then fused to formed a robust inference model. To validate our model, three specific physiological signals including Electromyogram (EMG), Blood Volume Pressure (BVP) and Galvanic Skin Response (GSR) recorded during eight emotional states were analyzed. Our approach showed 88.1% emotion recognition accuracy, which outperformed the conventional Support Vector Machine (SVM) classifier with 17% accuracy improvement. Furthermore, in order to avoid information redundancy and the resultant over-fitting, a feature reduction method is proposed based on a correlation analysis to optimize the number of features required for training and validating each weak learner. Despite the feature space dimensionality reduction from 27 to 18 features, our methodology preserved the recognition accuracy of about 85.0%.

In this paper, the use of mutual information and the Learn++.NSE algorithm is proposed to create an EEG SSVEP BCI system that can select and utilize data sets originating from a group of users. In typical BCI systems, the nonstationarity in the EEG prevents the system from blindly applying training data from other users to the incoming data. Mutual information is introduced to select previous data sets that provide the most information about current random variables. A signed rank test was employed to show that this configuration outperformed both normal Learn++.NSE ensembles and LDA classifiers. This indicates that mutual information and ensemble learning techniques may prove useful in improving user transferability in SSVEP systems with low computational requirements.

Obtaining labels can be costly and time-consuming. Active learning allows a learning algorithm to intelligently query samples to be labeled for a more efficient learning. Fisher information ratio (FIR) has been used as an objective for selecting queries. However, little is known about the theory behind the use of FIR for active learning. There is a gap between the underlying theory and the motivation of its usage in practice. In this paper, we attempt to fill this gap and provide a rigorous framework for analyzing existing FIR-based active learning methods. In particular, we show that FIR can be asymptotically viewed as an upper bound of the expected variance of the log-likelihood ratio. Additionally, our analysis suggests a unifying framework that not only enables us to make theoretical comparisons among the existing querying methods based on FIR, but also allows us to give insight into the development of new active learning approaches based on this objective.

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detect changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. We use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.

We show that mode-based cluster boundaries exhibit themselves as minor surfaces of the data probability density function. Based on this result, we provide a connectivity measure depending on minor surface search between sample pairs. Accordingly, we build a connectivity graph among data samples. The use of graph construction is particularly demonstrated for clustering, but applications in other machine learning areas are possible. On Gaussian mixture and kernel density estimate type probability density models, we illustrate the theoretical results with examples and demonstrate that cluster boundaries between sample pairs can be detected using a line integral. We also demonstrate an example where the data distribution has a continuous line segment as its set of local maxima (not strict), for which mean-shift like gradient flow and other mode-seeking algorithms fail to identify a single cluster, while the proposed approach successfully determines this fact.

Currently, many Brain Computer Interfaces (BCI) classifiers output point estimates of user intent which make it difficult to incorporate context prior information or assign a principled confidence measurement to a decision. We propose a Bayesian framework to extend current Steady State Visually Evoked Potential (SSVEP) classifiers to a maximum a posteriori (MAP) classifiers by using a Kernel Density Estimate (KDE) to learn the distribution of features conditioned on stimulation class. To demonstrate our framework we extend Canonical Correlation Analysis (CCA) and Power Spectral Density (PSD) style methods. Traditionally, in either example, the class is estimated as the class associated with the maximum feature. Our framework increases performance by relaxing the assumption that a stimulation class's sample often maximizes its class-associated feature. Further, by leveraging the KDE, we present a method which estimates the performance of a classifier under different stimulation frequency sets. Using this, we optimize the selection of stimulation frequencies from those present in a training set.

This paper extends previous work automatically detecting stereotypical motor movements (SMM) in individuals on the autism spectrum. Using three-axis accelerometer data obtained through wearable wireless sensors, we compare recognition results for two different classifiers -- Support Vector Machine and Decision Tree -- in combination with different feature sets based on time-frequency characteristics of accelerometer data. We use data collected from six individuals on the autism spectrum who participated in two different studies conducted three years apart in classroom settings, and observe an average accuracy across all participants over time ranging from 81.2% (TPR: 0.91; FPR: 0.21) to 99.1% (TPR: 0.99; FPR: 0.01) for all combinations of classifiers and feature sets. We also provide analyses of kinematic parameters associated with observed movements in an attempt to explain classifier-feature specific performance. Based on our results, we conclude that real-time, person-dependent, adaptive algorithms are needed in order to accurately and consistently measure SMM automatically in individuals on the autism spectrum over time in real-word settings.

Accurate multimodal and multisensor detection of a target phenomenon requires knowledge of probabilistic sensor characteristics to determine an appropriate fusion rule which optimizes an objective of interest, traditionally the expected Bayesian risk. However, a particular sensor characteristic can change online, introducing unaccounted additional risk to the fusion rule that was based on assumed sensor specifications. To mitigate such changes, we propose a sensor-failure-robust fusion rule assuming that only first order characteristics of a probabilistic sensor failure model are known. Under this failure model, we compute the expected Bayesian risk and minimize this risk to develop the proposed fusion method.

We consider the problem of target detection for coherent multi-input multi-output (MIMO) radar with widely separated antennas in the presence of phase synchronization mismatch between the transmitter and receiver pairs. First, we introduce a data model using von-Mises distribution to represent the phase error terms. Then we employ expectation-maximization algorithm to estimate the error distribution parameter and target returns as well as the noise variance. We develop a generalized likelihood ratio test (GLRT) target detector using these estimates and demonstrate the effect of phase uncertainties on the detection performance using Monte Carlo simulations.