请将以下内容进行压缩凝练（注意：可对内容进行删减，对错误进行修正，对语句顺序进行调整，符合美式英语标准，符合英语母语者语言习惯，句子简明易懂，术语使用准确，保留文章结构、不偏离论文主要内容）： Traditional machine learning methods can be divided into two categories: supervised and unsupervised learning methods. The supervised learning methods include Support Vector Machine (SVM), Random Forest, Decision Tree, etc. The unsupervised learning methods include Principal Component Analysis (PCA), Independent Component Analysis (ICA), K-means clustering, Non-Negative Matrix Decomposition (NMF), etc. Traditional machine learning methods also have shortcomings, which require high data quality, professional processing and feature engineering of data by experts according to experience and expertise, and high labor cost. At the same time, the use of traditional machine learning methods have its limitation because they need to be combined with the geological situation of a certain region to target anomaly detection, and cannot be universally applied to other regions.

Traditional machine learning can be divided into supervised and unsupervised methods, such as Support Vector Machines, Random Forests, Decision Trees, Principal Component Analysis, Independent Component Analysis, K-means clustering, and Non-Negative Matrix Decomposition. However, these methods have limitations and require high-quality data, expert feature engineering, and are often region-specific for anomaly detection.

相关问题

以下是一篇即将投稿Minerals期刊（MDPI出版社）的论文初稿的部分内容，请按照该期刊对论文格式的要求，将以下内容进行压缩凝练（注意：可对内容进行删减，对错误进行修正，对语句顺序进行调整，符合美式英语标准，符合英语母语者语言习惯，句子简明易懂，术语使用准确，保留文章结构、不偏离论文主要内容）： Rocks and ore components directly enter the soil and water system sediments through physical weathering and chemical weathering, and the geochemical anomalies originally present in the rocks further spread with the entry into the soil or directly into the water system, forming soil anomalies and water system sediment anoma-lies.Geochemical anomaly detection is essentially the detection of signal anomalies in geochemical data, which refers to finding out the anomalous distribution of chemical elements themselves and the anomalous distribution of multiple elements in combination through feature extraction and analysis processing of geochemical data in the study area, and reflecting the mineral distribution through the distribution of geochemical ele-ments.Through the method of geochemical anomaly finding, the detected anomalies may contain information indicating specific minerals, which facilitates the rapid tracing of prospective areas and favorable areas for mineralization, identifies possible mineralizing elements and distribution characteristics in the work area, provides basic information for the strategic deployment of mineralization search, and provides good indications for later mineralization search.

Rocks and ore components enter soil and water system sediments through physical and chemical weathering, causing the spread of geochemical anomalies. Detecting geochemical anomalies involves identifying anomalous chemical element distributions through feature extraction and analysis of geochemical data. These anomalies can reveal specific minerals and their distribution characteristics, aiding in the identification of prospective mineralization areas and elements. Geochemical anomaly detection provides valuable information for strategic mineralization search deployment and subsequent exploration efforts.

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