Deep kernelized dimensionality reducer for multi-modality heterogeneous data

Abstract

Data mining applications use high-dimensional datasets, but still, a large number of extents causes the well-known ‘Curse of Dimensionality,' which leads to worse accuracy of machine learning classifiers due to the fact that most unimportant and unnecessary dimensions are included in the dataset. Many approaches are employed to handle critical dimension datasets, but their accuracy suffers as a result. As a consequence, to deal with high-dimensional datasets, a hybrid Deep Kernelized Stacked De-Noising Auto encoder based on feature learning was proposed (DKSDA). Because of the layered property, the DKSDA can manage vast amounts of heterogeneous data and performs knowledge-based reduction by taking into account many qualities. It will examine all the multimodalities and all hidden potential modalities using two fine-tuning stages, the input has random noise along with feature vectors, and a stack of de-noising auto-encoders is generated. This SDA processing decreases the prediction error caused by the lack of analysis of concealed objects among the multimodalities. In addition, to handle a huge set of data, a new layer of Spatial Pyramid Pooling (SPP) is introduced along with the structure of Convolutional Neural Network (CNN) by decreasing or removing the remaining sections other than the key characteristic with structural knowledge using kernel function. The recent studies revealed that the DKSDA proposed has an average accuracy of about 97.57% with a dimensionality reduction of 12%. By enhancing the classification accuracy and processing complexity, pre-training reduces dimensionality.