46 research outputs found
Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model
Gaussian Mixture Models (GMM) are one of the most potent parametric density
estimators based on the kernel model that finds application in many scientific
domains. In recent years, with the dramatic enlargement of data sources,
typical machine learning algorithms, e.g. Expectation Maximization (EM),
encounters difficulty with high-dimensional and streaming data. Moreover,
complicated densities often demand a large number of Gaussian components. This
paper proposes a fast online parameter estimation algorithm for GMM by using
first-order stochastic optimization. This approach provides a framework to cope
with the challenges of GMM when faced with high-dimensional streaming data and
complex densities by leveraging the flexibly-tied factorization of the
covariance matrix. A new stochastic Manifold optimization algorithm that
preserves the orthogonality is introduced and used along with the well-known
Euclidean space numerical optimization. Numerous empirical results on both
synthetic and real datasets justify the effectiveness of our proposed
stochastic method over EM-based methods in the sense of better-converged
maximum for likelihood function, fewer number of needed epochs for convergence,
and less time consumption per epoch
Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction
Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method
Context Transfer in Reinforcement Learning Using Action-Value Functions
This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agentsâ MDPs can be mapped. This is formulated in terms of the notion of MDP homomorphism. The learning framework is Q-learning. To transfer the knowledge between these tasks, the feature space is used as a translator and is expressed as a partial mapping between the state-action spaces of different tasks. The Q-values learned during the learning process of the source tasks are mapped to the sets of Q-values for the target task. These transferred Q-values are merged together and used to initialize the learning process of the target task. An interval-based approach is used to represent and merge the knowledge of the source tasks. Empirical results show that the transferred initialization can be beneficial to the learning process of the target task
Abstract Concept Learning Approach Based on Behavioural Feature Extraction
Hosseini B, Ahmadabadi MN, Araabi BN. Abstract Concept Learning Approach Based on Behavioural Feature Extraction. In: Kamaruzaman J, ed. 2009 Second International Conference on Computer and Electrical Engineering. Vol 2. Piscataway, NJ: IEEE; 2010
Bayesian Dynamic DAG Learning: Application in Discovering Dynamic Effective Connectome of Brain
Understanding the complex mechanisms of the brain can be unraveled by
extracting the Dynamic Effective Connectome (DEC). Recently, score-based
Directed Acyclic Graph (DAG) discovery methods have shown significant
improvements in extracting the causal structure and inferring effective
connectivity. However, learning DEC through these methods still faces two main
challenges: one with the fundamental impotence of high-dimensional dynamic DAG
discovery methods and the other with the low quality of fMRI data. In this
paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity
characterization \textbf{(BDyMA)} method to address the challenges in
discovering DEC. The presented dynamic causal model enables us to discover
bidirected edges as well. Leveraging an unconstrained framework in the BDyMA
method leads to more accurate results in detecting high-dimensional networks,
achieving sparser outcomes, making it particularly suitable for extracting DEC.
Additionally, the score function of the BDyMA method allows the incorporation
of prior knowledge into the process of dynamic causal discovery which further
enhances the accuracy of results. Comprehensive simulations on synthetic data
and experiments on Human Connectome Project (HCP) data demonstrate that our
method can handle both of the two main challenges, yielding more accurate and
reliable DEC compared to state-of-the-art and baseline methods. Additionally,
we investigate the trustworthiness of DTI data as prior knowledge for DEC
discovery and show the improvements in DEC discovery when the DTI data is
incorporated into the process
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Differences in white matter reflect atypical developmental trajectory in autism: A Tract-based Spatial Statistics studyâ
Autism is a neurodevelopmental disorder in which white matter (WM) maturation is affected. We assessed WM integrity in 16 adolescents and 14 adults with high-functioning autism spectrum disorder (ASD) and in matched neurotypical controls (NT) using diffusion weighted imaging and Tract-based Spatial Statistics. Decreased fractional anisotropy (FA) was observed in adolescents with ASD in tracts involved in emotional face processing, language, and executive functioning, including the inferior fronto-occipital fasciculus and the inferior and superior longitudinal fasciculi. Remarkably, no differences in FA were observed between ASD and NT adults. We evaluated the effect of age on WM development across the entire age range. Positive correlations between FA values and age were observed in the right inferior fronto-occipital fasciculus, the left superior longitudinal fasciculus, the corpus callosum, and the cortical spinal tract of ASD participants, but not in NT participants. Our data underscore the dynamic nature of brain development in ASD, showing the presence of an atypical process of WM maturation, that appears to normalize over time and could be at the basis of behavioral improvements often observed in high-functioning autism
A Clustering Method Based on Soft Learning of Model (Prototype) and Dissimilarity Metrics
Many clustering methods are designed for especial cluster types or have good performance dealing with particular size and shape of clusters. The main problem in this connection is how to define a similarity (or dissimilarity) criterion to make an algorithm capable of clustering general data, which include clusters of different shape and size. In this paper a new approach to fuzzy clustering is proposed, in which during learning a model for each cluster is estimated. Gradually besides, dissimilarity metric for each cluster is defined, updated and used for the next step. In our approach, instead of associating a single cluster type to each cluster, we assume a set of possible cluster types for each cluster with different grades of possibility. Also proposed method has the capability to deal with partial labeled data. Comparing the experimental results of this method with several important existing algorithms, demonstrates the superior performance of proposed method. The merit of this method is its ability to deal with clusters of different shape and size