近日,电子科技大学信息与通信工程学院刘翼鹏副教授、刘佳妮博士生、龙珍博士生和朱策教授历时两年所著的《Tensor Computation for Data Analysis》由施普林格(Springer)出版集团正式出版。该书讨论了张量计算推广的一系列机器学习方法,详细讲解了张量计算基础,全方面多层次地介绍了张量计算方法在数据分析方面的各种应用,可以...
Liu Y, Liu J, Long Z, Zhu C (2022) Tensor computation for data analysis. Springer, Berlin Book Google Scholar Zhang C, Fu H, Liu S, Liu G, Cao X (2015) Low-rank tensor constrained multiview subspace clustering. In: Proceedings of the IEEE international conference on computer vision....
Independent component analysis finds latent variables that are statistically independent in observed data. The two related demos illustrate the computation of basic as well as constrained CPD. Read more Independent Vector Analysis Independent vector analysis is a multi-set extension of independent component...
Tensor Computation for Data Analysis Yipeng Liu, Jiani Liu, Zhen Long & Ce Zhu 3328 Accesses Abstract Sketching is a group of dimensionality reduction approaches which succinctly approximate the original data using random projections or samplings. In contrast to general random subsamplings, the ...
(I admire the elegance of your method of computation; it must be nice to ride through these fields upon the horse of true mathematics while the like of us have to make our way laboriously on foot.) 更重要的是,近现代大多物理规律都是用张量进行表达的,若不懂张量,可能很难懂相对论,很难懂...
Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation t ... read full description Purchase book Share this bookBrowse content ...
Processing data where it makes sense: enabling in-memory computation. Microprocess. Microsyst. 67, 28–41 (2019). Article Google Scholar Kang, M., Keel, M.-S., Shanbhag, N. R., Eilert, S. & Curewitz, K. An energy-efficient VLSI architecture for pattern recognition via deep embedding...
As computing power increases, many more problems in engineering and data analysis involve computation with tensors, or multi-way data arrays. Most applications involve computing a decomposition of a tensor into a linear combination of rank-1 tensors. Ideally, the decomposition involves a minimal num...
The limited computation budget for the forward problem therefore leads to flattening of the high-dimensional landscape of the likelihoods, wiping off the structural information that should be used to navigate the optimisation algorithm towards the solution of (4). This motivates the development of ...
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