Dan Zhang

An experimental evaluation of ensemble methods for EEG signal classification

Ensemble learning for improving weak classifiers is one important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. They have so far shown efficacies in many practical classification problems. However, for electroencephalogram (EEG) signal classification with application to brain-computer interfaces (BCIs), there are almost no studies investigating their feasibilities. The present study systematically evaluates the performance of the three ensemble methods for EEG signal classification of mental imagery tasks. With the base classifiers of k-nearest-neighbor, decision tree and support vector machine, classification experiments are carried out upon real EEG recordings. Experimental results suggest the feasibilities of ensemble classification methods, and we also derive some valuable conclusions on the performance of ensemble methods for EEG signal classification.

Interactive localized content based image retrieval with multiple-instance active learning

In this paper, we propose two general multiple-instance active learning (MIAL) methods, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple-instance active learning with fisher information (F-MIAL), and apply them to the active learning in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one, while F-MIAL utilizes the fisher information and analyzes the value of the unlabeled pictures by assigning different labels to them. In experiments, we will show their superior performances in LCBIR tasks.

Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, tumor segmentation work is difficult. This paper introduces a discriminative classification algorithm for semi-automated segmentation of brain tumorous tissues. The classifier uses interactive hints to obtain models to classify normal and tumor tissues. A non-parametric Bayesian Gaussian random field in the semi-supervised mode is implemented. Our approach uses both labeled data and a subset of unlabeled data sampling from 2D/3D images for training the model. Fast algorithm is also developed. Experiments show that our approach produces satisfactory segmentation results comparing to the manually labeled results by experts.

Letters: MACBSE: Extracting signals with linear autocorrelations

This paper proposes blind source extraction methods based on several time-delay autocorrelations of primary sources, called MACBSE. The MACBSE approaches are batch fixed-point learning algorithms for extraction of source signals with linear autocorrelations. The fixed-point algorithms are very simple and do not need to choose any learning step sizes. Furthermore, the convergence properties of the algorithms are analyzed. Their efficiencies are demonstrated by extensive computer simulations.

Localized content-based image retrieval using semi-supervised multiple instance learning

In this paper, we propose a Semi-Supervised Multiple-Instance Learning (SSMIL) algorithm, and apply it to Localized Content-Based Image Retrieval (LCBIR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art algorithms is promising.

Localized content based image retrieval by multiple instance active learning

In this paper, we propose two general multiple instance active learning (MIAL) algorithms, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple- instance active learning with fisher information (F-MIAL), and apply them to the relevance feedback in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one, while F-MIAL can utilize the fisher information and analyze the value of the unlabeled pictures by assigning different labels to them. We show that F-MIAL can be integrated more naturally into the multiple instance learning scenario. In experiments, we will show their superior performances on some real-world image datasets.

Multi-Pitch Estimation Based on Partial Event and Support Transfer

This paper proposes a method for the multi-pitch estimation of polyphonic music signals. Instead of on the frame level, the estimation is based on the partial event, which is defined like the note event in MIDI. All partial events in a piece of music are extracted dynamically in the process of the frame by frame short time Fourier transform (STFT). For each event, net support degree received from other events is calculated and the events with the highest support degrees are selected to be the fundamental frequency (FO) events. From another point of view, the support is transferred from higher frequency partial events to lower ones and finally concentrated on the FO events. This method can estimate the number of concurrent sounds, the onset and offset times of the notes. Experiments on both randomly mixed chord signals and synthesized ensemble music signals in wav format are conducted and the results are promising.

A Fixed-Point Algorithm for Blind Separation of Temporally Correlated Sources

In this paper we develop a new method for blind separation of temporally correlated sources, possibly dependent signals from linear mixtures of them. The proposed algorithm is based on the mutual independency of the innovations of source signals instead of original signals. This algorithm takes into account both the temporal structure and the high-order statistics of source signals and in contrast to the most known blind separation algorithms only exploiting the second order statistics or the non-Gaussianity. In this framework, a fixed-point algorithm is introduced. The fixed-point algorithm is computationally very simple, converge fast, and does not need choose any learning step sizes. Extensive computer simulations with speech signals and images confirm the validity and high performance of the proposed algorithm.