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Joint blind source separation (JBSS) techniques have proven to be a natural solution for achieving source separation of multiple data sets. JBSS algorithms, such as independent vector analysis (IVA), are a promising alternative to independent component analysis (ICA) based approaches for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Unlike ICA, little is known about...
A new theory for kernel entropy component analysis (kernel ECA) is developed, based on distribution dependent convolution operators, ensuring the validity of the method for any positive semi-definite kernel. Furthermore, a new semi-supervised kernel ECA classification method is derived with positive results compared to the state-of-the-art.
Reconstruction of depth from 2D images is an important research issue in computer vision. Depth from defocus (DFD) technique uses space varying blurring of an image as a cue in reconstructing the 3D structure of a scene. In this paper we explore the regularization based approach for simultaneous estimation of depth and image restoration from defocused observations. We are given two defocused observations...
In this work we aim to learn a Mahalanobis distance to improve the performance of phoneme classification using the standard 39-dimensional MFCC features. To learn and to evaluate the performance of our distance, we use the simple k-nearest-neighbors (k-NN) classifier. Although this classifier exhibits low performance relative to state-of-the-art phoneme classifiers, it can be used to determine a distance...
Local field potentials (LFPs) arise from dendritic currents that are summed by the brain tissue's impedance. By assuming that the rhythms existing in the LFPs result from the coordinated neural activity of sparse and transient neural assemblies transformed by the neural tissue, we propose to recover these neural assemblies sources using an independent component analysis on segments of a single LFP...
In large ad-hoc networks, classification tasks such as spam filtering, multi-camera surveillance, and advertising have been traditionally implemented in a centralized manner by means of fusion centers. These centers receive and process the information that is collected from across the network. In this paper, we develop a decentralized adaptive strategy for information processing and apply it to the...
During tracking process in optical flow, some points are normally easy to be lost or become outliers, due to the fact that the object undergoes changes of illumination or becomes partially occluded. The paper presents a novel scheme, Heterogeneity Elimination Individually (HEI), for outlier rejections from the tracking results of optical flow. HEI determines the most poisonous element by the distance...
In this paper, we propose a novel image segmentation method based on manifold spectral clustering. This method is based on the simple idea that image can be represented as the set of several manifolds which are also referred as super-pixels, and thus image segmentation problem are solved by manifold clustering. Based on this idea, we have designed a novel manifold spectral clustering method for image...
In this paper, we propose an effective method to automatically diagnose coronary heart disease by detecting ST segment episodes of ECG signals. To improve the diagnostic accuracy, we consider the motion activity of individual while monitoring ECG signals and we detect the motion activity of people through heart rate. Our method is based on clinical principle that ST segment depression is greater relative...
Human preferences can effectively be elicited using pairwise comparisons and in this paper current state-of-the-art based on binary decisions is extended by a new paradigm which allows subjects to convey their degree of preference as a continuous but bounded response. For this purpose, a novel Beta-type likelihood is proposed and applied in a Bayesian regression framework using Gaussian Process priors...
Information theory allows one to pose problems in principled terms that very often have direct interpretation. For instance, capturing the structure based on statistical regularities of data can be thought of as a problem of relevance determination, that is, information preservation under limited resources. The principle of relevant information is an information theoretic objective function that attempts...
Topic models are of broad interest. They can be used for query expansion and result structuring in information retrieval and as an important component in services such as recommender systems and user adaptive advertising. In large scale applications both the size of the database (number of documents) and the size of the vocabulary can be significant challenges. Here we discuss two mechanisms that...
We evaluate recent Gaussian process (GP)-based manifold learning methods for human motion modeling, including our recently proposed joint gait and pose manifolds (JGPMs). Unlike most GP algorithms that involve either one latent variable or multiple independent variables in separate latent spaces, JGPMs define two variables jointly and explicitly in one latent space to represent a collection of gait...
This paper considers underdetermined blind source separation of super-Gaussian signals that are convolutively mixed. The separation is performed in three stages. In the first stage, the mixing matrix in each frequency bin is estimated by the proposed single source detection and clustering (SSDC) algorithm. In the second stage, by assuming complex-valued super-Gaussian distribution, the sources are...
This paper presents a novel algorithm for independent vector analysis (IVA) of Gaussian data sets. Following a maximum likelihood (ML) approach, we show that the cost function to be minimized by the proposed GML-IVA algorithm reduces to an estimate of the mutual information among the different sets of latent variables. The proposed method, which can be seen as a new generalization of canonical correlation...
This paper describes a method to learn demand models and find relative locations of users and devices based on usage logs only. It therefore allows the monitoring and optimization of infrastructures using a signal that is often already available. Absolute positions can be obtained by combining the usage logs with a small number of hand-labeled positions of users and/or devices.
The operator-based signal separation approach, which formulates the signal separation as an optimization problem, uses an adaptive operator to separate a signal into additive subcomponents. Furthermore, it is possible to design different operators to fit different signal models. In this paper, we propose a new kind of differential operator to separate multicomponent AM-FM signals. We then use the...
The performance of the complex-valued blind source separation (BSS) is studied in the frequency domain approach to separate convolutive speech mixtures. In this context, the strong uncorrelating transform (SUT) and complex maximization of non-Gaussianity (CMN) do not produce satisfactory separation results since their assumptions about the independence among the frequency-domain complex-valued sources...
In this paper we introduce a kernel-based recursive least-squares (KRLS) algorithm that is able to track nonlinear, time-varying relationships in data. To this purpose we first derive the standard KRLS equations from a Bayesian perspective (including a principled approach to pruning) and then take advantage of this framework to incorporate forgetting in a consistent way, thus enabling the algorithm...
Signal processing algorithms in Wireless Sensor Networks claim for energy efficiency because of node energy scarcity. Tailored to this scenario, in this paper we develop energy-efficient cooperative strategies for selective communications. Cooperation among nodes is exploited in order to optimize energy consumption while guaranteeing good overall performance. The analysis of representative scenarios...
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