Probability Product Kernel Research Articles

Supported Diagnosis of Attention Deficit and Hyperactivity Disorder from EEG Based on Interpretable Kernels for Hidden Markov Models.

As a neurodevelopmental pathology, Attention Deficit Hyperactivity Disorder (ADHD) mainly arises during childhood. Persistent patterns of generalized inattention, impulsivity, or hyperactivity characterize ADHD that may persist into adulthood. The conventional diagnosis relies on clinical observational processes yielding high rates of overdiagnosis due to varying interpretations among specialists or missing information. Although several studies have designed objective behavioral features to overcome such an issue, they lack significance. Despite electroencephalography (EEG) analyses extracting alternative biomarkers using signal processing techniques, the nonlinearity and nonstationarity of EEG signals restrain performance and generalization of hand-crafted features. This work proposes a methodology to support ADHD diagnosis by characterizing EEG signals from hidden Markov models (HMM), classifying subjects based on similarity measures for probability functions, and spatially interpreting the results using graphic embeddings of stochastic dynamic models. The methodology learns a single HMM for EEG signal from each patient, so favoring the inter-subject variability. Then, the Probability Product Kernel, specifically developed for assessing the similarity between HMMs, fed a support vector machine that classifies subjects according to their stochastic dynamics. Lastly, the kernel variant of Principal Component Analysis provided a means to visualize the EEG transitions in a two-dimensional space, evidencing dynamic differences between ADHD and Healthy Control children. From the electrophysiological perspective, we recorded EEG under the Stop Signal Task modified with reward levels, which considers cognitive features of interest as insufficient motivational circuits recruitment. The methodology compares the supported diagnosis in two EEG channel setups (whole channel set and channels of interest in frontocentral area) and four frequency bands (Theta, Alpha, Beta rhythms, and a wideband). Results evidence an accuracy rate of 97.0% in the Beta band and in the channels where previous works found error-related negativity events. Such accuracy rate strongly supports the dual pathway hypothesis and motivational deficit concerning the pathophysiology of ADHD. It also demonstrates the utility of joining inhibitory and motivational paradigms with dynamic EEG analysis into a noninvasive and affordable diagnostic tool for ADHD patients.

Semantic concept model using Wikipedia semantic features

Wikipedia has become a high coverage knowledge source which has been used in many research areas such as natural language processing, text mining and information retrieval. Several methods have been introduced for extracting explicit or implicit relations from Wikipedia to represent semantics of concepts/words. However, the main challenge in semantic representation is how to incorporate different types of semantic relations to capture more semantic evidences of the associations of concepts. In this article, we propose a semantic concept model that incorporates different types of semantic features extracting from Wikipedia. For each concept that corresponds to an article, four semantic features are introduced: template links, categories, salient concepts and topics. The proposed model is based on the probability distributions that are defined for these semantic features of a Wikipedia concept. The template links and categories are the document-level features which are directly extracted from the structured information included in the article. On the other hand, the salient concepts and topics are corpus-level features which are extracted to capture implicit relations among concepts. For the salient concepts feature, the distributional-based method is utilised on the hypertext corpus to extract this feature for each Wikipedia concept. Then, the probability product kernel is used to improve the weight of each concept in this feature. For the topic feature, the Labelled latent Dirichlet allocation is adapted on the supervised multi-label of Wikipedia to train the probabilistic model of this feature. Finally, we used the linear interpolation for incorporating these semantic features into the probabilistic model to estimate the semantic relation probability of the specific concept over Wikipedia articles. The proposed model is evaluated on 12 benchmark datasets in three natural language processing tasks: measuring the semantic relatedness of concepts/words in general and in the biomedical domain, semantic textual relatedness measurement and measuring the semantic compositionality of noun compounds. The model is also compared with five methods that depends on separate semantic features in Wikipedia. Experimental results show that the proposed model achieves promising results in three tasks and outperforms the baseline methods in most of the evaluation datasets. This implies that incorporation of explicit and implicit semantic features is useful for representing semantics of concepts in Wikipedia.

Spine labeling in axial magnetic resonance imaging via integral kernels

This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images.

Object tracking in video via particle filter

In this paper, we present a new method for object tracking. We use an efficient local search scheme based on the probability product kernel using particle filter (PPKPF) to find the image region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed algorithm.

Object tracking in video via particle filter

In this paper, we present a new method for object tracking. We use an efficient local search scheme based on the probability product kernel using particle filter PPKPF to find the image region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed algorithm.

An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel

We present a new method for object tracking; we use an efficient local search scheme based on the Kalman filter and the probability product kernel (KFPPK) to find the image region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system.

Speaker Identification With Whispered Speech for the Access Control System

This work presents an access control system, which is a speaker identification system based on whispered speech. Speaker identification is a main function of an access control system. Hence, a novel speaker identification system using instantaneous frequencies is proposed. The input speech signals pass through both signal independent and signal dependent filters firstly. Then, we derive the signal’s instantaneous frequencies by applying the Hilbert transform. The analyzed instantaneous frequencies are proceeded to be modeled as probability density models. We use these probability density models as the feature in the proposed speaker identification system. In this work, we compare the use of parametric and nonparametric probability density estimation for instantaneous frequency modeling. Furthermore, we propose an approximated probability product kernel support vector machine (APPKSVM). In the APPKSVM, Riemann sum is applied in approximating the probability product kernel. The whisper sounds from the CHAIN speech corpus were used in the experiments. Results of the experiments show the superiority of the proposed speaker identification system.

Robust Environmental Sound Recognition With Fast Noise Suppression for Home Automation

This paper proposes a robust environmental sound recognition system using a fast noise suppression approach for home automation applications. The system comprises a fast subspace-based noise suppression module and a sound classification module. For the noise suppression module, we propose a noise suppression method that applies fast subspace approximations in the wavelet domain. We show that this method offers a lower computational cost than conventional methods. In the sound classification module, we use a feature extraction method that is also based on the wavelet subspace, derived from seventeen critical bands in a signal's wavelet packet transform. Furthermore, we create a multiclass support vector machine by employing probability product kernels. The experimental results for ten classes of various environmental sounds show that the proposed system offers robust performance in environmental sound recognition tasks.

Music emotion detection using hierarchical sparse kernel machines.

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

Trajectory clustering in CCTV traffic videos using probability product kernels with hidden Markov models

This article presents a method for clustering the trajectories obtained by tracking vehicles in traffic videos, recorded from CCTV cameras in public spaces. The proposed method employs a model-based approach in which (1) each trajectory (position and velocity) is modelled using a hidden Markov model (HMM), and (2) the distance between two trajectories is computed as the probabilistic similarity between HMMs, by means of the probability product kernel. Experiments on a set of real traffic video sequences reveal very good results of the proposed approach, outperforming two non-trivial baselines. To the best of the authors' knowledge, this approach is novel for trajectory grouping in traffic videos.

Automatic speaker age and gender recognition using acoustic and prosodic level information fusion

The paper presents a novel automatic speaker age and gender identification approach which combines seven different methods at both acoustic and prosodic levels to improve the baseline performance. The three baseline subsystems are (1) Gaussian mixture model (GMM) based on mel-frequency cepstral coefficient (MFCC) features, (2) Support vector machine (SVM) based on GMM mean supervectors and (3) SVM based on 450-dimensional utterance level features including acoustic, prosodic and voice quality information. In addition, we propose four subsystems: (1) SVM based on UBM weight posterior probability supervectors using the Bhattacharyya probability product kernel, (2) Sparse representation based on UBM weight posterior probability supervectors, (3) SVM based on GMM maximum likelihood linear regression (MLLR) matrix supervectors and (4) SVM based on the polynomial expansion coefficients of the syllable level prosodic feature contours in voiced speech segments. Contours of pitch, time domain energy, frequency domain harmonic structure energy and formant for each syllable (segmented using energy information in the voiced speech segment) are considered for analysis in subsystem (4). The proposed four subsystems have been demonstrated to be effective and able to achieve competitive results in classifying different age and gender groups. To further improve the overall classification performance, weighted summation based fusion of these seven subsystems at the score level is demonstrated. Experiment results are reported on the development and test set of the 2010 Interspeech Paralinguistic Challenge aGender database. Compared to the SVM baseline system (3), which is the baseline system suggested by the challenge committee, the proposed fusion system achieves 5.6% absolute improvement in unweighted accuracy for the age task and 4.2% for the gender task on the development set. On the final test set, we obtain 3.1% and 3.8% absolute improvement, respectively.

Modeling adaptive kernels from probabilistic phylogenetic trees

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Finding Relevant Linear Manifolds in Classification by Gaussian Mixtures

In this article, we present a strategy for producing low-dimensional projections that maximally separate the classes in Gaussian Mixture Model classification. The most revealing linear manifolds are those along which the classes are maximally separable. Here we consider a particular probability product kernel as a measure of similarity or affinity between the class-conditional distributions. It takes an appealing closed analytical form in the case of Gaussian mixture components. The performance of the proposed strategy has been evaluated on real data.

Fast Schemes for Computing Similarities between Gaussian HMMs and Their Applications in Texture Image Classification

An appropriate definition and efficient computation of similarity (or distance) measures between two stochastic models are of theoretical and practical interest. In this work, a similarity measure, that is, a modified generalized probability product kernel, of Gaussian hidden Markov models is introduced. Two efficient schemes for computing this similarity measure are presented. The first scheme adopts a forward procedure analogous to the approach commonly used in probability evaluation of observation sequences on HMMs. The second scheme is based on the specially defined similarity transition matrix of two Gaussian hidden Markov models. Two scaling procedures are also proposed to solve the out-of-precision problem in the implementation. The effectiveness of the proposed methods has been evaluated on simulated observations with predefined model parameters, and on natural texture images. Promising experimental results have been observed.

Probability Product Kernels

The advantages of discriminative learning algorithms and kernel machines are combined with generative modeling using a novel kernel between distributions. In the probability product kernel, data po...