Prediction of underwater acoustic signals based on ESMD and ELM

357-362The local predictability of underwater acoustic signals plays an important role in underwater acoustic signal processing, as it is the basis for solving non-stationary signal detection. A prediction model of underwater acoustic signals based on extreme-point symmetric mode decomposition (ESMD) and extreme learning machine (ELM) is proposed. First, underwater acoustic signals are decomposed by ESMD to obtain a set of intrinsic model functions (IMFs). After IMFs are grouped, the training samples and forecast samples are obtained. Then, prediction model for training samples is established by using ELM to obtain the input layer, output layer weight vector and offset matrix. The trained ELM is used to predict the forecast sample to obtain component. Finally, the reconstructed IMFs and residuals are the final prediction results. The experimental results show that the proposed model is a good predictive model having better prediction accuracy and smaller error

Prediction of underwater acoustic signals based on ESMD and ELM

357-362The local predictability of underwater acoustic signals plays an important role in underwater acoustic signal processing, as it is the basis for solving non-stationary signal detection. A prediction model of underwater acoustic signals based on extreme-point symmetric mode decomposition (ESMD) and extreme learning machine (ELM) is proposed. First, underwater acoustic signals are decomposed by ESMD to obtain a set of intrinsic model functions (IMFs). After IMFs are grouped, the training samples and forecast samples are obtained. Then, prediction model for training samples is established by using ELM to obtain the input layer, output layer weight vector and offset matrix. The trained ELM is used to predict the forecast sample to obtain component. Finally, the reconstructed IMFs and residuals are the final prediction results. The experimental results show that the proposed model is a good predictive model having better prediction accuracy and smaller error

Positive Semidefinite Metric Learning Using Boosting-like Algorithms

The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive definite. Semidefinite programming is often used to enforce this constraint, but does not scale well and easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare favorably to those state-of-the-art methods in terms of classification accuracy and running time.Comment: 30 pages, appearing in Journal of Machine Learning Researc