GTT: Guiding the Tensor Train Decomposition
GTT: Guiding the Tensor Train Decomposition
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GTT：指导张量序列分解
DOI：
10.1007/978-3-030-60936-8_15
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发表时间：
2020
影响因子：
3.3
通讯作者：
M. Sapino
中科院分区：
文献类型：
--
作者：
Springer Science;Business Media;Deutschland Gmbh;Mao;K. Candan;M. Sapino
.人们对图像、视频、音频等多媒体数据的搜索、查询需求无处不在，如何高效地访问数据是各种应用的一个关键问题。然而，这些数据通常被编码为多维数组，或张量，传统的数据挖掘技术可能会受到限制，由于维数灾难。张量分解被提出来缓解这个问题，常用的张量分解算法包括CP-分解（寻找对角核）和Tucker-分解（寻找稠密核）。当然，塔克保留了更多的信息，但由于核心的密集性，它也会随着张量模式的数量呈指数增长。张量训练（TT）分解通过寻找一系列三模式核来解决这个问题：但不幸的是，目前还没有选择分解序列的指导方针。在本文中，我们提出了一个GTT方法来指导张量列车在选择的分解序列。GTT利用数据特征（包括模式的数量、各个模式的长度、密度、互信息的分布和熵的分布）以及目标分解秩来选择将保留信息的分解顺序。不同数据集的实验结果表明，GTT有效地引导TT分解过程的分解序列，更好地保持准确性。
. The demand for searching, querying multimedia data such as image, video and audio is omnipresent, how to effectively access data for various applications is a critical task. Nevertheless, these data usually are encoded as multi-dimensional arrays, or Tensor , and traditional data mining techniques might be limited due to the curse of dimensionality . Tensor decomposition is proposed to alleviate this issue, commonly used tensor decomposition algorithms include CP-decomposition (which seeks a diagonal core) and Tucker-decomposition (which seeks a dense core). Naturally, Tucker maintains more information, but due to the denseness of the core, it also is subject to exponential memory growth with the number of tensor modes. Tensor train ( TT ) decomposition addresses this problem by seeking a sequence of three-mode cores: but unfortunately, currently, there are no guidelines to select the decomposition sequence. In this paper, we propose a GTT method for guiding the tensor train in selecting the decomposition sequence. GTT leverages the data characteristics (including number of modes, length of the individual modes, density, distribution of mutual information, and distribution of entropy) as well as the target decomposition rank to pick a decomposition order that will preserve information. Experiments with various data sets demonstrate that GTT effectively guides the TT-decomposition process towards decomposition sequences that better preserve accuracy.
BICP：具有更新敏感细化的块增量 CP 分解
DOI：
10.1145/2983323.2983717
发表时间：
2016
期刊：
CIKM
影响因子：
--
作者：
Huang, Shengyu;Candan, K. Selçuk;Sapino, Maria Luisa
通讯作者：
Sapino, Maria Luisa