25 citations · 32 across the 14 of their papers we have counts for
12 papers · 1 filter
FusAD: Time-Frequency Fusion with Adaptive Denoising for General Time Series Analysis
Da Zhang, Bingyu Li, Zhiyuan Zhao +3
Time series analysis plays a vital role in fields such as finance, healthcare, industry, and meteorology, underpinning key tasks including classification, forecasting, and anomaly…
UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting
Da Zhang, Bingyu Li, Zhuyuan Zhao +3
As multimodal data proliferates across diverse real-world applications, leveraging heterogeneous information such as texts and timestamps for accurate time series forecasting (TSF)…
FAIM: Frequency-Aware Interactive Mamba for Time Series Classification
Da Zhang, Bingyu Li, Zhiyuan Zhao +4
Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition. TSC tasks require mod…
Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework
Guanxiong He, Jie Wang, Liaoyuan Tang +3
Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are for…
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Jinghui Yuan, Fangyuan Xie, Feiping Nie +1
The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this r…
Double-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clusterin
Fangyuan Xie, Jinghui Yuan, Feiping Nie +1
Min cut is an important graph partitioning method. However, current solutions to the min cut problem suffer from slow speeds, difficulty in solving, and often converge to simple so…
A Greedy Strategy for Graph Cut
Feiping Nie, Shenfei Pei, Zengwei Zheng +2
We propose a Greedy strategy to solve the problem of Graph Cut, called GGC. It starts from the state where each data sample is regarded as a cluster and dynamically merges the two…
Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method
Feiping Nie, Yitao Song, Wei Chang +2
In the graph-based semi-supervised learning, the Green-function method is a classical method that works by computing the Green's function in the graph space. However, when applied…
Clustering Based on Density Propagation and Subcluster Merging
Feiping Nie, Yitao Song, Jingjing Xue +2
We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unl…
PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting
Yongbo Yu, Weizhong Yu, Feiping Nie +1
The self-attention mechanism in Transformer architecture, invariant to sequence order, necessitates positional embeddings to encode temporal order in time series prediction. We arg…
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
Jinghui Yuan, Weijin Jiang, Zhe Cao +4
Ensemble learning is a method that leverages weak learners to produce a strong learner. However, obtaining a large number of base learners requires substantial time and computation…
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Jinghui Yuan, Chusheng Zeng, Fangyuan Xie +5
Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within this domain. Doubly stochastic symmetri…