2011 IEEE International Workshop on Machine Learning for Signal Processing

The 21st IEEE International Workshop on Machine Learning for Signal Processing will be held in Beijing, China, on September 18–21, 2011. The workshop series is the major annual technical event of the IEEE Signal Processing Society's Technical Committee on Machine Learning for Signal Processing. This year the workshop is held in the National Laboratory of Pattern Recognition (NLPR), Institute of Au... View full abstract»

Provides a listing of current committee members. View full abstract»

The functions of proteins are closely related to their subcellular locations. Computational methods are required to replace the laborious and time-consuming experimental processes for proteomics research. This paper proposes combining homology-based profile alignment methods and functional-domain based Gene Ontology (GO) methods to predict the subcellular locations of proteins. The feature vectors... View full abstract»

A multi-resolution speech and audio sinusoidal analysis/synthesis model based on an improved Empirical Mode Decomposition (EMD) is proposed in this paper. Because of the special filtering characteristic and superiority in dealing with non-stationary signal of EMD, a preprocessing module is adopted to classify the original signal by using the energy ratio and spectrum center of each Intrinsic Mode ... View full abstract»

Within the framework of the strategic research project Xsense at the Technical University of Denmark, we are developing a colorimetric sensor array which can be useful for detection of explosives like DNT, TNT, HMX, RDX and TATP and identification of volatile organic compounds in the presence of water vapor in air. In order to analyze colorimetric sensors with statistical methods, the sensory outp... View full abstract»

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 pr... View full abstract»

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 r... View full abstract»

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 ab... View full abstract»

We propose an ICA based algorithm for spike sorting in multi-channel neural recordings. In such context, the performance of ICA is known to be limited since the number of recording sites is much lower than the number of the neurons around. The algorithm uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity o... View full abstract»

A novel and robust approach for applause and laugh detection is proposed based on sparse representation features and heterogeneous mixture models (hetMM). The projections of the noise robust sparse representations for audio signals computed by L₁ - minimization are used as feature. We consider the classifiers based on heterogeneous mixture models (hetMM) which combine multip... View full abstract»

Playfield detection is a key technology for content analysis in sports video, on which many semantic clue mining methods rely. However, shadow produced by substantial illumination change causes the general detection method fail and degenerate the performance of the following processing based on it. This paper presents a method for detecting playfield, which can find shadow region under the guidanc... View full abstract»

This paper presents a metric measurement approach from sequences of images captured from a moving spherical camera without the need of additional equipment, such as laser scanners or motion detection units. The user assists the algorithms with simple inputs to facilitate the measurement process. The operator initially selects a keyframe that contains the object of interest that is to be measured. ... View full abstract»

Hidden Markov models are widely used for modeling underlying dynamics of sequence data. The accurate hidden state estimation is one of the central issues on practical application since the dynamics is described as a sequence of hidden states. However, while there are many studies on parameter estimation, mathematical properties of the hidden state estimation have not been clarified yet. The presen... View full abstract»

Vector of locally aggregated descriptors (VLAD) has overcome the lossy quantization of bag-of-words model (BOW), but its dimensionality is high for direct use. We reduce the dimensionality of VLAD by a special coding scheme. First descriptors are clustered, and then linear discriminant analysis (LDA) is performed separately within each cluster. For different cluster, we allow different dimensional... View full abstract»

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. We identify the associated primal problem and develop a f... View full abstract»

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. View full abstract»

Active learning has been a popular area of research in recent years. It can be used to improve the performance of learning tasks by asking the labels of unlabeled data from the user. In these methods, the goal is to achieve the highest possible accuracy gain while posing minimum queries to the user. The existing approaches for active learning have been mostly applicable to the traditional binary o... View full abstract»

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 ... View full abstract»

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... View full abstract»

For cameras mounted on a vehicle, the estimation of the vanishing point corresponding to the observed field of view is an important machine vision task necessary for a lot of applications, such as camera calibration and autonomous vehicle navigation. In this paper, a novel method for the estimation of the vanishing point corresponding to a particular camera orientation with respect to the vehicle ... View full abstract»

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 gai... View full abstract»