# Kernelized Multiview Subspace Analysis by Self-weighted Learning

arXiv: https://arxiv.org/abs/1911.10357 · HTML: https://mlsanity.com/paper/1911.10357/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2019.

## Built on by (most cited first, 18 of 18)

- [Multi-view MERA Subspace Clustering](https://mlsanity.com/paper/2305.09095/) (2023)
- [Multi-view Clustering via Deep Matrix Factorization and Partition Alignment](https://mlsanity.com/paper/2105.00277/) (2021)
- [Reachable Distance Function for KNN Classification](https://mlsanity.com/paper/2103.09704/) (2021)
- [Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance](https://mlsanity.com/paper/2405.10987/) (2024)
- [Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering](https://mlsanity.com/paper/2301.02484/) (2023)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)
- [Latent Semantic Consensus For Deterministic Geometric Model Fitting](https://mlsanity.com/paper/2403.06444/) (2024)
- [CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data](https://mlsanity.com/paper/2203.06551/) (2022)
- [Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations](https://mlsanity.com/paper/2210.13570/) (2022)
- [Multimodal-Aware Weakly Supervised Metric Learning with Self-weighting Triplet Loss](https://mlsanity.com/paper/2102.02670/) (2021)
- [Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement](https://mlsanity.com/paper/2105.00248/) (2021)
- [Locality Relationship Constrained Multi-view Clustering Framework](https://mlsanity.com/paper/2107.05073/) (2021)
- [Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification](https://mlsanity.com/paper/2204.13323/) (2022)
- [Multi-view Feature Extraction based on Dual Contrastive Head](https://mlsanity.com/paper/2302.03932/) (2023)
- [Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection](https://mlsanity.com/paper/2302.06494/) (2023)
- [Multi-view Feature Extraction based on Triple Contrastive Heads](https://mlsanity.com/paper/2303.12615/) (2023)
- [Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos](https://mlsanity.com/paper/2308.04322/) (2023)
- [Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design](https://mlsanity.com/paper/2407.07611/) (2024)

## Abstract (arXiv)

With the popularity of multimedia technology, information is always represented or transmitted from multiple views. Most of the existing algorithms are graph-based ones to learn the complex structures within multiview data but overlooked the information within data representations. Furthermore, many existing works treat multiple views discriminatively by introducing some hyperparameters, which is undesirable in practice. To this end, abundant multiview based methods have been proposed for dimension reduction. However, there are still no research to leverage the existing work into a unified framework. To address this issue, in this paper, we propose a general framework for multiview data dimension reduction, named Kernelized Multiview Subspace Analysis (KMSA). It directly handles the multi-view feature representation in the kernel space, which provides a feasible channel for direct manipulations on multiview data with different dimensions. Meanwhile, compared with those graph-based methods, KMSA can fully exploit information from multiview data with nothing to lose. Furthermore, since different views have different influences on KMSA, we propose a self-weighted strategy to treat different views discriminatively according to their contributions. A co-regularized term is proposed to promote the mutual learning from multi-views. KMSA combines self-weighted learning with the co-regularized term to learn appropriate weights for all views. We also discuss the influence of the parameters in KMSA regarding the weights of multi-views. We evaluate our proposed framework on 6 multiview datasets for classification and image retrieval. The experimental results validate the advantages of our proposed method.