Joint Label-Density-Margin Space and Extreme Elastic Net for Label-Specific Features

Abstract

The label-specific features learning is a kind of framework for extracting the specific features of each label for classification. At present, the label-specific features algorithm is generally based on the original label space to find a particular feature set. This kind of extraction method for label-specific features has a general adaptation when the label density is balanced. However, in most multi-label data sets, the number of positive and negative labels varies greatly, and the label density is unbalanced. And there is an inherent correlation between each label. When extracting label-specific features, the method needs to consider issues such as imbalanced label density and label correlations. Based on this, a label-specific features learning method based on Joint Label-density-margin Space and Extreme Elastic Net (JLSE2N) is proposed in this paper. Firstly, to enlarge the margin between the positive and negative labels in the original label space, the positive and negative label density of the total example are calculated. And the information of unbalanced label density is integrated into the label space, thus forming a priori knowledge of the label-density-margin space. Subsequently, to quickly extract the label-specific features, the elastic net regularization extreme learning machine is used for the first time to obtain the label-specific features. The transformed label density space is used to calculate the cosine similarity and is added to the L 2 regularization term to consider the correlation between pairs of labels. The L 1 regularization term in the elastic net can generate the sparse weight matrix to extract the required label-specific features. We compare the proposed method with five well-known algorithms on 11 benchmark data sets. Experimental analysis shows that our proposed method is superior to the state-of-the-art algorithms for multi-label learning.

A review on extreme learning machine

“…The OAL1-ELM can be trained on one-by-one samples or in batch mode. Pei and Wang [135] used ELM for label-specific feature learning.…”

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

“…To deal with degraded raw data, the back-propagation neural network and weight application to failure times (WAFT) prediction technique are used to establish the rolling bearing prediction model. In [22], a RUL forecasting approach was presented by utilizing competitive learning, where the statistical properties obtained by using the continuous wavelet transform (CWT) to deal with the data were taken as an input of the recurrent neural network (RNN). The similar defect propagation stages of the monitored bearing are represented by clustering the input data.…”