Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1410.0993

Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization

Le Li·Jianjun Yang·Yang Xu·Zhen Qin·Honggang Zhang
View original source ↗Read source document ↗Export metadata ↓

Abstract

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the $l_2$ distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of two low-rank matrices for document clustering. This method also allows us to learn the new basis vectors of the semantic feature space from the data. To our knowledge, we haven't seen any work has been done by maximizing correntropy in NMF to cluster high dimensional document data. Our experiment results show the supremacy of our proposed method over other variants of NMF algorithm on Reuters21578 and TDT2 databasets.

Explore related subjects

cs.IR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Le Li, Jianjun Yang, Yang Xu, Zhen Qin, Honggang Zhang. 2014-10-03. Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization. https://arxiv.org/abs/1410.0993

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Cross-Document Neural Re-Ranking via Query-Induced Subgraphs

Neural re-rankers typically score query-document pairs independently, neglecting cross-document context within the retrieved candidate set. We propose Graph Neural Re-Ranking (GNRR), a framework that extracts a sparse, query-induced subgraph from a pre-computed semantic corpus graph and applies Graph Neural Networks (GNN) to propagate cross-document signals. Unlike self-attention re-rankers, which scale quadratically with the number of candidates ($\mathcal{O}(K^2)$), GNRR achieves $\mathcal{O}(c \cdot K)$ online complexity, where $c$ is the fixed corpus graph degree and $K$ the candidate set size. We evaluate five GNN operators within this framework and find that architecture choice substantially affects generalization to harder queries: the GCN variant is the only one that consistently improves over TCT-ColBERT across all three TREC benchmarks. On TREC-DLHard, the most challenging evaluation benchmark, GNRR achieves $+5.2\%$ relative AP over TCT-ColBERT and $+9.0\%$ AP over a self-attention re-ranker. Notably, self-attention re-ranking degrades AP on DLHard ($-3.5\%$ versus TCT-ColBERT), suggesting that sparse corpus-graph structure provides a complementary re-ranking signal that dense self-attention fails to capture. Efficiency analysis shows that GNN models require fewer parameters and lower per-query latency at $K=1000$ than self-attention, with linear rather than quadratic scaling in candidate set size. Code to reproduce our experiment is available at https://github.com/difra100/Graph-Neural-Re-Ranking-via-Corpus-Graph

cs.IR↗
arXiv2026

LazFormer: Scaling Transformers for Industrial Recommendation via Transferable Generative Pre-training

Transformers have shown promising performance in LLMs due to their outstanding scalability, several studies have investigated the scalability of Transformers for industrial recommendation. They typically rely on a single ranking model to optimize both sparse and dense parameters from scratch, resulting in substantial computational resource consumption and slow convergence. Fortunately, the pre-training models offer an effective solution to the above issues by providing favorable initialization of both sparse and dense parameters for the subsequent ranking. However, they still face two major limitations: (1) Since the input features used in pre-training and ranking are usually inconsistent, directly transferring dense parameters from pre-training to ranking may lead to negative transfer. (2) Multi-epoch training during the ranking process may result in the overfitting of sparse parameters, while freezing the sparse parameters limits their adaptability to the ranking objectives. To this end, we propose a Scaling Transformer for Industrial Recommendation via Transferable Generative Pre-training, termed LazFormer. Specifically, we first present a generative pre-training module to autoregressively generate sequential features, providing favorable initialization of both sparse and dense parameters for the subsequent ranking. To solve the negative transfer of dense parameters, we propose a transferable residual adapter that injects additional ranking-specific features into ranking in a residual manner. Moreover, a request-aware ranking module integrates long-sequence compression, hybrid sparse attention, and a request-aware paradigm to efficiently model users' long sequences. Besides, we further propose an asymmetric multi-epoch training strategy that resets sparse parameters while continuously accumulating dense parameters across epochs, alleviating the overfitting of sparse parameters.

cs.IR↗
arXiv2026

Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation

In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user population, particularly suppressing the needs of long-tail audiences. While Large Language Models (LLMs) have been integrated into recommendation for content understanding or ranking, directly optimizing them to output a single best headline typically leads to mode collapse---converging to generic patterns that satisfy average tastes but miss specific latent intents. To bridge this gap, we introduce GESE (Generate to Explore, Select to Exploit), a framework operating at the system's presentation layer that decouples personalization into generative exploration and selective exploitation. First, we treat the LLM as a probabilistic explorer, utilizing Group Sequence Policy Optimization (GSPO) with a hierarchical reward mechanism to generate a candidate set that maximizes the semantic coverage of potential user interests. Subsequently, a lightweight, real-time feedback-aware selector acts as the exploiter, identifying the optimal realization from the candidate pool based on instant contextual signals. Extensive deployment on a commercial platform with over 100 million daily active users demonstrates that GESE significantly outperforms state-of-the-art baselines, achieving a 2.57% lift in CTR and 0.87% in dwell time. These results validate that decoupling diversity-oriented generation from precision-oriented selection offers a robust blueprint for aligning generative AI with dynamic user utility.

cs.IR↗