6 papers · 1 filter
Beyond MSE: Ordinal Cross-Entropy for Probabilistic Time Series Forecasting
Jieting Wang, Huimei Shi, Feijiang Li +1
Time series forecasting is an important task that involves analyzing temporal dependencies and underlying patterns (such as trends, cyclicality, and seasonality) in historical data…
RI-Loss: A Learnable Residual-Informed Loss for Time Series Forecasting
Jieting Wang, Xiaolei Shang, Feijiang Li +1
Time series forecasting relies on predicting future values from historical data, yet most state-of-the-art approaches-including transformer and multilayer perceptron-based models-o…
Ranked Set Sampling-Based Multilayer Perceptron: Improving Generalization via Variance-Based Bounds
Feijiang Li, Liuya Zhang, Jieting Wang +2
Multilayer perceptron (MLP), one of the most fundamental neural networks, is extensively utilized for classification and regression tasks. In this paper, we establish a new general…
Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion
Liang Du, Henghui Jiang, Xiaodong Li +5
Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown p…
k-HyperEdge Medoids for Clustering Ensemble
Feijiang Li, Jieting Wang, Liuya zhang +4
Clustering ensemble has been a popular research topic in data science due to its ability to improve the robustness of the single clustering method. Many clustering ensemble methods…
Deep Embedding Clustering Driven by Sample Stability
Zhanwen Cheng, Feijiang Li, Jieting Wang +1
Deep clustering methods improve the performance of clustering tasks by jointly optimizing deep representation learning and clustering. While numerous deep clustering algorithms hav…