Broad Learning Model with a Dual Feature Extraction Strategy for Classification

Abstract

The broad learning system (BLS) is a brief, flat neural network structure that has shown effectiveness in various classification tasks. However, original input data with high dimensionality often contain superfluous and correlated information affecting recognition performance. Moreover, the large number of randomly mapped feature nodes and enhancement nodes may also cause a risk of redundant information that interferes with the conciseness and performance of the broad learning paradigm. To address the above-mentioned issues, we aim to introduce a broad learning model with a dual feature extraction strategy (BLM_DFE). In particular, kernel principal component analysis (KPCA) is applied to process the original input data before extracting effective low-dimensional features for the broad learning model. Afterwards, we perform KPCA again to simplify the feature nodes and enhancement nodes in the broad learning architecture to obtain more compact nodes for classification. As a result, the proposed model has a more straightforward structure with fewer nodes and retains superior recognition performance. Extensive experiments on diverse datasets and comparisons with various popular classification approaches are investigated and evaluated to support the effectiveness of the proposed model (e.g., achieving the best result of 77.28%, compared with 61.44% achieved with the standard BLS, on the GT database).