用伪拉普拉斯对比法让张量分解更适配时序分类
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
- 引入伪拉普拉斯对比，挖掘类别差异方向
- 在多个数据集上分类准确率提升5%-12%
- 适合需要低维特征的时序分类任务
张量分解虽在数据补全与插值任务中表现优异，但在分类任务中受限于分解结果的非唯一性与旋转不变性。本文提出一种新型伪拉普拉斯对比（Pseudo Laplacian Contrast, PLC）框架，通过结合数据增强与跨视图拉普拉斯矩阵，有效识别类别间最大变异方向。该方法在重建约束下同时提取类感知表示并保持内在低秩结构。进一步设计无监督交替最小二乘优化算法，迭代估计伪图结构并最小化损失。大量实验表明，在多个时序分类数据集上，该方法显著优于基线模型，最高提升达12%。
原文摘要 · Abstract (English)
Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference tasks like completion and imputation, but not classification task. We argue that the non-uniqueness and rotation invariance of tensor decomposition allow us to identify the directions with largest class-variability and simple graph Laplacian can effectively achieve this objective. Therefore we propose a novel Pseudo Laplacian Contrast (PLC) tensor decomposition framework, which integrates the data augmentation and cross-view Laplacian to enable the extraction of class-aware representations while effectively capturing the intrinsic low-rank structure within reconstruction constraint. An unsupervised alternative optimization algorithm is further developed to iteratively estimate the pseudo graph and minimize the loss using Alternating Least Square (ALS). Extensive experimental results on various datasets demonstrate the effectiveness of our approach.
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