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The IEEE International workshop on Machine Learning for Signal Processing (MLSP) is the main annual event organized by the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society.
The goal of the MLSP 2014 competition was to automatically detect subjects with schizophrenia based on multimodal features derived from magnetic resonance imaging data. This report summarizes the 3rd place solution with the final ROC area score of 0.91282.
The goal of the MLSP 2014 Schizophrenia Classification Challenge was to automatically diagnose subjects with schizophrenia based on multimodal features derived from their magnetic resonance imaging (MRI) brain scans. This challenge took place between June 5 and July 20, 2014, and was organized on Kaggle. We present how this classification problem can be solved in terms of a Bayesian machine learning...
The goal of the MLSP 2014 Classification Challenge was to automatically detect subjects with schizophrenia and schizoaffective disorder based on multimodal features derived from the magnetic resonance imaging (MRI) data. The patients with age range of 18–65 years were diagnosed according to DSM-IV criteria. The training data consisted of 46 patients and 40 healthy controls. The test set included 119...
In this paper, we investigate the problem of detecting depression from recordings of subjects' speech using speech processing and machine learning. There has been considerable interest in this problem in recent years due to the potential for developing objective assessments from real-world behaviors, which may provide valuable supplementary clinical information or may be useful in screening. The cues...
Vector quantization (VQ) is a data compression method in machine learning and data mining field by representing a larger data set with a smaller number of vectors in a possible way. Several vector quantization algorithms have been proposed in recent years. Different from the classic vector quantization algorithms such as LBG and K-means, the algorithms based on information theoretic learning try to...
The ability to monitor or even to predict the performance level of a subject when engaged in a cognitive task can be useful in various real-life scenarios. In this article we focus on a popular EEG-based Brain Computer Interface (BCI) paradigm and report on the complexity of the EEG signals in relation to the subject's performance level. We estimate signal complexity with a multivariate, multiscale...
Multikernel adaptive filtering has recently attracted significant research interest due to its enhanced flexibility and adaptation performance over single-kernel methods. In this paper, we focus on convex combinations of two single-kernel adaptive filters, characterized by different convergence speeds and steady-state performances, in order to get the best of both. We consider online estimation using...
We proposed a new scheme on automatic annotation and analysis for songs of Bengalese finches, that have variability in terms of syllable sequencing. The scheme annotates songs by using the beta process hidden Markov model, a Bayesian non-parametrics method. The annotation was confirmed to agree to the results by the manual annotation by an expert almost perfectly (0.81–1.00) for songs by three out...
Inter-subject alignment is an important aspect of multi-subject fMRI research. Recently a method known as Hyperalignment has shown considerable success in attaining such alignment. In order to improve computational efficiency, we investigate a joint SVD-Hyperalignment algorithm. We show that this algorithm is more scalable than the standard Hyperalignment algorithm by providing analytic and empirical...
While the more learning data the better the recognition, increase in the data causes an expensive computational cost in learning. This paper proposes how to decrease the computational cost by appropriately selecting the learning data. In particular, we put our focus on learning for human pose estimation in still images. Three kinds of methods are proposed for learning data selection in this paper...
Gaussian graphical models are of great interest in statistical learning. Since the conditional independence between the variables corresponds to zero entries in the inverse covariance matrix, one can learn the structure of the graph by estimating a sparse inverse covariance matrix from sample data. This is usually done by solving a convex maximum likelihood problem with a l1-regularization term applied...
This paper deals with the principal component analysis in networks, where it is improper to compute the sample covariance matrix. To this end, we derive several in-network strategies to estimate the principal axes, including noncooperative and cooperative (diffusion-based) strategies. The performance of the proposed strategies is illustrated on diverse applications, including image processing and...
Recently, a new kernel-based approach for identification of time-invariant linear systems has been proposed. Working under a Bayesian framework, the impulse response is modeled as a zero-mean Gaussian vector, with covariance given by the so called stable spline kernel. Such a prior model encodes smoothness and exponential stability information, and depends just on two unknown parameters that can be...
Sensor selection in nonparametric decentralized detection is investigated. Kernel-based minimization framework with a weighted kernel is adopted, where the kernel weight parameters represent sensors' contributions to decision making. L1 regularization on weight parameters is introduced into the risk function so that the resulting optimal decision rule contains a sparse vector of nonzero weight parameters...
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