A Study on Category Classification Based on LightGBM for Signal Feature Extraction and K-Means Clustering

LightGBM is a framework for implementing the GBDT algorithm, which supports efficient parallel training and has the advantages of faster training speed, lower memory consumption, better accuracy, support for distributed. It has the advantages of faster training speed, lower memory consumption, better accuracy, distributed support, etc. It can handle large amount of data quickly. Dimensionality reduction is a method of pre-processing high-dimensional feature data. Dimensionality reduction is to keep the most important features of the high-dimensional data and remove the noise and unimportant features, so as to achieve the purpose of improving the speed of data processing. The K-Means algorithm is an algorithm for cluster analysis, which is mainly used to calculate data aggregation, mainly by continuously taking the nearest mean value to the seed point.