Unsupervised Minimum Redundancy Maximum Relevance Feature Selection for Predictive Maintenance

Identifying and selecting optimal prognostic health indicators in the context of predictive maintenance is essential to obtain a good model and make accurate predictions. Several metrics have been proposed in the past decade to quantify the relevance of those prognostic parameters. Other works have used the well-known minimum redundancy maximum relevance (mRMR) algorithm to select features that are both relevant and non-redundant. However, the relevance criterion is based on labelled machine malfunctions which are not always available in real life scenarios. In this paper, we develop a prognostic mRMR feature selection, an adaptation of the conventional mRMR algorithm, to a situation where class labels are a priori unknown, which we call unsupervised feature selection. In addition, this paper proposes new metrics for computing the relevance and compares different methods to estimate redundancy between features. We show that using unsupervised feature selection as well as adapting relevance metrics with the dynamic time warping algorithm help increase the effectiveness of the selection of health indicators for a rotating machine case study.

Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance

Surface electromyography- (sEMG-) based gesture recognition is widely used in rehabilitation training, artificial prosthesis, and human-computer interaction. The purpose of this study is to simplify the sEMG devices by reducing channels while achieving comparably high gesture recognition accuracy. We propose a compound channel selection scheme by combining the variable selection algorithms based on multitask sparse representation (MTSR) and minimum Redundancy Maximum Relevance (mRMR). Specifically, channelwise features are first extracted to compose channel-feature paired variables, for which variable selection procedures by MTSR and mRMR are carried out, respectively. Then, we rank all the channels according to their occurrences in each variable selection procedure and figure out a certain number of informative channels by fusing these rankings of channels. Finally, the gesture classification performance using the selected channels is evaluated by the support vector machine (SVM) classifier. Experiment results validate the effectiveness of this proposed method.

Mutual Information Based Framework of Quality Parameter Formulation for Similarity Index between Two Musical Notes

Thepaper presents an approach of formulating a parameter to access the relative quality of a musical note with reference to the standard musical note. The tonal quality perceived by an individual is prominently subjective. The work in the paper focusses on the objectivity of this comparison. The objectivity introduced essentially will be helpful to come up with at least basic conclusive step regarding the relative tonal quality. The proposed parameter is also evaluated against the rating given by the raters to the tonal quality of a musical note with reference to reference note to prove its effectiveness and agreement with raters. Harmonium and flute are taken as the instruments for experimentation. The approach proposed can be applied to any two musical notes produced with different instruments with one instrument reference. The quality parameter is proposed by evaluating different parameters of musical note and then using the technique of minimum redundancy maximum relevance (mRmR) technique of feature ranking. The Empirical way is formulated to develop the quality parameter which is also put to test against rankings of raters. Thus the paper provide a systematic way of deducing the quality parameter which will be in agreement with the tonal quality perceived by human ears.