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An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the...
This paper presents a novel dimensionality reduction method, called uncorrelated transferable feature extraction (UTFE), for signal classification in brain-computer interfaces (BCIs). Considering the difference between the source and target distributions of signals from different subjects, we construct an optimization objective that finds a projection matrix to transform the original data in a high-dimensional...
Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction intervals (PIs) are well...
Encoded Neural Networks (ENN) associate low-complexity algorithm with a storage capacity much larger than Hopfield Neural Networks' (HNN) for the same number of nodes. They are thus promising for implementing large scale neural networks mimicking the functioning of the human brain. The implementation of such a network on chip requires reducing the power consumption of the nodes to the femtojoule range...
The mammalian visual system is uniquely capable of robustly recognizing objects in its field of view regardless of their orientation, scale, or position, while learning new objects from a small number of training examples and generalizing robustly to a broad class of visually similar objects. The cortical structures that implement the visual system have been successfully emulated in several biologically-inspired...
We are interested in modelling musical pitch sequences in melodies in the symbolic form. The task here is to learn a model to predict the probability distribution over the various possible values of pitch of the next note in a melody, given those leading up to it. For this task, we propose the Recurrent Temporal Discriminative Restricted Boltzmann Machine (RTDRBM). It is obtained by carrying out discriminative...
Pulse rate and rhythm are indicators of the health of a human's blood circulation. Being able to detect one's pulse rate and rhythm in an emergency situation could be the difference between life and death. The work presented in this paper is preliminary work on algorithms that will equip a robot with the necessary skills to assess a human's pulse. Algorithms for pulse detection and the calculation...
Frequency estimation in three-phase power systems is considered from a state space point of view, and a robust and fast converging algorithm for estimating the fundamental frequency of three-phase power systems is introduced. This is achieved by exploiting the Clarke transform to incorporate the information from all the phases and then designing a widely linear state space estimator that can accurately...
Cutting-plane methods are well-studied localization (and optimization) algorithms. We show that they provide a natural framework to perform machine learning —and not just to solve optimization problems posed by machine learning— in addition to their intended optimization use. In particular, they allow one to learn sparse classifiers and provide good compression schemes. Moreover, we show that very...
Recognizing the semantic content of an image is a challenging problem in computer vision. Many researchers attempt to apply local image descriptors to extract features from an image, but choosing the best type of feature to use is still an open problem. Some of these systems are only trained once using a fixed descriptor, like the Scale Invariant Feature Transform (SIFT). In most cases these algorithms...
In this paper, a nearly optimal neuro-controller is developed by using adaptive dynamic programming (ADP) for the single shaft heavy-duty gas turbine (GT). Unlike the conventional controllers, the neuro-controller consists of two neural network (NN) structures: the critic and action network. The critic network learns to approximate the cost-to-go or strategic utility function and uses the output of...
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task. However, the long time needed to train such deep networks is a major drawback. We tackled this problem by reusing a previously trained network...
We address the problem of detecting anomalies in images, specifically that of detecting regions characterized by structures that do not conform those of normal images. In the proposed approach we exploit convolutional sparse models to learn a dictionary of filters from a training set of normal images. These filters capture the structure of normal images and are leveraged to quantitatively assess whether...
Automatic facial expression recognition plays an important role in agent-based interface development and datadriven animation. This paper presents an intelligent facial action and emotion recognition system for a humanoid robot. Motivated by the Facial Action Coding System, this research focuses on the recognition of seven basic emotions and 18 Action Units (AU). Since effective facial representations...
In this paper, we propose Multi-state Activation Functions (MSAFs) for Deep Neural Networks (DNNs). These multi-state functions do extra classification based on the 2-state Logistic function. Discussions on the MSAFs reveal that these activation functions have potentials for altering the parameter distribution of the DNN models, improving model performances and reducing model sizes. Meanwhile, an...
The development of fast and mobile drug detection is an important aspect of personalized medicine. It enables the quick assessment of inter-individual differences in drug metabolism and corresponding adjustments of the dose. Recent developments of amperometric biosensors using cytochrome P450 (CYP) show great promise, by lowering the detection limit to physiological range for several drugs via the...
The k-nearest neighbor method generates predictions for a particular instance from its neighborhood. It is a simple but effective supervised method for classification. However, the traditional k-nearest neighbor algorithm using the majority voting rule for the class label usually loses a part of useful information in the neighborhood. This paper tries to learn from the neighborhood for more useful...
In order to extend the unsupervised nonlinear dimensionality reduction method Isomap for use in supervised learning, a new supervised manifold learning method namely discriminant Isomap (D-Isomap) is proposed, in which the geometrical structure of each class data is preserved by keeping geodesic distances between data points of the same class and the discriminant capacity is enhanced by maximizing...
It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often biased, measurement to determine if an instance is noisy. A biased measure may work well on certain data sets, but it can also be less effective on a broader set of data sets. In this paper, we conduct a large empirical...
Real time pattern recognition applications often deal with high dimensional data, which require a data reduction step which is only performed offline. However, this loses the possibility of adaption to a changing environment. This is also true for other applications different from pattern recognition, like data visualization for input inspection. Only linear projections, like the principal component...
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