Rectified Gaussian kernel multi-view k-means clustering

2024-05-09 · Kristina P. Sinaga

In this paper, we show two new variants of multi-view k-means (MVKM) algorithms to address multi-view data. The general idea is to outline the distance between $h$-th view data points $x_i^h$ and $h$-th view cluster centers $a_k^h$ in a different manner of centroid-based approach. Unlike other methods, our proposed methods learn the multi-view data by calculating the similarity using Euclidean norm in the space of Gaussian-kernel, namely as multi-view k-means with exponent distance (MVKM-ED). By simultaneously aligning the stabilizer parameter $p$ and kernel coefficients $\beta^h$, the compression of Gaussian-kernel based weighted distance in Euclidean norm reduce the sensitivity of MVKM-ED. To this end, this paper designated as Gaussian-kernel multi-view k-means (GKMVKM) clustering algorithm. Numerical evaluation of five real-world multi-view data demonstrates the robustness and efficiency of our proposed MVKM-ED and GKMVKM approaches.

📄 PDF Abstract BibTeX arXiv:2405.05619

Code (1)

PatternKPS/RectifiedMVKM 공식 구현

Tasks

ClusteringMultiview Clustering

Methods 이 논문이 사용한 방법론

k-Means Clustering k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$…

Rectified Euler k-means and Beyond

2021-08-06 · Yunxia Lin, Songcan Chen

Euler k-means (EulerK) first maps data onto the unit hyper-sphere surface of equi-dimensional space via a complex mapping which induces the robust Euler kernel and next employs the popular $k$-means. Consequently, beside…

Invariance of Weight Distributions in Rectified MLPs

2017-11-24 · ICML 2018 7 · Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher

An interesting approach to analyzing neural networks that has received renewed attention is to examine the equivalent kernel of the neural network. This is based on the fact that a fully connected feedforward network wit…

Sparsity-depth Tradeoff in Infinitely Wide Deep Neural Networks

2023-05-17 · Chanwoo Chun, Daniel D. Lee

We investigate how sparse neural activity affects the generalization performance of a deep Bayesian neural network at the large width limit. To this end, we derive a neural network Gaussian Process (NNGP) kernel with rec…

regression

Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations

2026-02-01 · Yilun Kuang, Yash Dagade, Tim G. J. Rudner, Randall Balestriero 외 arxiv

Joint-Embedding Predictive Architectures (JEPA) learn view-invariant representations and admit projection-based distribution matching for collapse prevention. Existing approaches regularize representations towards isotro…

Image Classification

Bayesian Deconditional Kernel Mean Embeddings

2019-06-01 · Kelvin Hsu, Fabio Ramos

Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex models. However, the recovery of the or…

Gaussian Processes