Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering

• URL: http://arxiv.org/abs/2405.16447v1
• Date: Sun, 26 May 2024 06:29:12 GMT
• Title: Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering
• Authors: Yan Chen, Liang Du, Lei Duan,
• Abstract summary: Multiple Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of information from multiple base kernels for clustering. We propose Efficient Multiple Kernel Concept Factorization (EMKCF), which constructs a new sparse kernel matrix inspired by local regression to achieve memory efficiency.
• Score: 5.21777096853979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Kernel methods are extensively employed for nonlinear data clustering, yet their effectiveness heavily relies on selecting suitable kernels and associated parameters, posing challenges in advance determination. In response, Multiple Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of information from multiple base kernels for clustering. However, both early fusion and late fusion methods for large-scale MKC encounter challenges in memory and time constraints, necessitating simultaneous optimization of both aspects. To address this issue, we propose Efficient Multiple Kernel Concept Factorization (EMKCF), which constructs a new sparse kernel matrix inspired by local regression to achieve memory efficiency. EMKCF learns consensus and individual representations by extending orthogonal concept factorization to handle multiple kernels for time efficiency. Experimental results demonstrate the efficiency and effectiveness of EMKCF on benchmark datasets compared to state-of-the-art methods. The proposed method offers a straightforward, scalable, and effective solution for large-scale MKC tasks.

Related papers

• Multiple kernel concept factorization algorithm based on global fusion [9.931283387968856]
  In unsupervised environment, to design or select proper kernel function for specific dataset, a new algorithm called Globalized Multiple Kernel(GMKCF)was proposed. The proposed algorithm outperforms comparison algorithms in data clustering, such as Kernel K-Means(KKM), Spectral Clustering(SC), CF Kernel(KCF), Co-regularized multi-view spectral clustering(Coreg), and Robust Multiple KKM(RMKKM)
  arXiv  Detail & Related papers  (2024-10-27T09:13:57Z)
• MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence [97.93517982908007]
  In cross-domain few-shot classification, NCC aims to learn representations to construct a metric space where few-shot classification can be performed. In this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. We propose a bi-level optimization framework, emphmaximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data.
  arXiv  Detail & Related papers  (2024-05-29T05:59:52Z)
• Kernel Correlation-Dissimilarity for Multiple Kernel k-Means Clustering [21.685153346752124]
  Current methods enhance information diversity and reduce redundancy by exploiting interdependencies among multiple kernels based on correlations or dissimilarities. We introduce a novel method that systematically integrates both kernel correlation and dissimilarity. By emphasizing the coherence between kernel correlation and dissimilarity, our method offers a more objective and transparent strategy for extracting non-linear information.
  arXiv  Detail & Related papers  (2024-03-06T04:24:43Z)
• One-Step Late Fusion Multi-view Clustering with Compressed Subspace [29.02032034647922]
  We propose an integrated framework named One-Step Late Fusion Multi-view Clustering with Compressed Subspace (OS-LFMVC-CS) We use the consensus subspace to align the partition matrix while optimizing the partition fusion, and utilize the fused partition matrix to guide the learning of discrete labels.
  arXiv  Detail & Related papers  (2024-01-03T06:18:30Z)
• Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel [22.23550794664218]
  This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework. The framework incorporates a quantized alternating direction method of multipliers (ADMM) for collaborative learning among multiple agents. Experiments on diverse datasets demonstrate the superior prediction performance and efficiency of our proposed methods.
  arXiv  Detail & Related papers  (2023-09-15T07:05:33Z)
• Late Fusion Multi-view Clustering via Global and Local Alignment Maximization [61.89218392703043]
  Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering. We propose late fusion MVC via alignment to address these issues.
  arXiv  Detail & Related papers  (2022-08-02T01:49:31Z)
• Asymmetric Scalable Cross-modal Hashing [51.309905690367835]
  Cross-modal hashing is a successful method to solve large-scale multimedia retrieval issue. We propose a novel Asymmetric Scalable Cross-Modal Hashing (ASCMH) to address these issues. Our ASCMH outperforms the state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.
  arXiv  Detail & Related papers  (2022-07-26T04:38:47Z)
• Multiple Kernel Clustering with Dual Noise Minimization [56.009011016367744]
  Multiple kernel clustering (MKC) aims to group data by integrating complementary information from base kernels. In this paper, we rigorously define dual noise and propose a novel parameter-free MKC algorithm by minimizing them. We observe that dual noise will pollute the block diagonal structures and incur the degeneration of clustering performance, and C-noise exhibits stronger destruction than N-noise.
  arXiv  Detail & Related papers  (2022-07-13T08:37:42Z)
• Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph [73.68184322526338]
  Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. This paper proposes a novel local sample-weighted multiple kernel clustering model. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algo-rithms.
  arXiv  Detail & Related papers  (2022-07-05T05:00:38Z)
• SimpleMKKM: Simple Multiple Kernel K-means [49.500663154085586]
  We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM) Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error.
  arXiv  Detail & Related papers  (2020-05-11T10:06:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.