Linear Algebra Tools for Data Mining (Second Edition)

This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications.The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases.

這本更新的彙編提供了理解和開發數據挖掘和機器學習中的線性代數應用所需的背景知識。基礎知識和先進的新主題（譜理論、奇異值、矩陣、張量和多維數組的分解技術）與線性代數的幾個應用一起呈現（k-means聚類、雙標圖、最小二乘逼近、降維技術、張量和多維數組）。這本有用的參考書包括600多個練習和補充材料，其中許多有完整的解答和MATLAB應用。這本書對於模式識別/圖像分析、人工智能、機器學習和數據庫領域的專業人士、學者、研究人員和研究生都有益處。