arXiv research

Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also been well developed, which could efficiently explore higher-order neighborhoods via multi-hop random walks. However, despite the success of …

SMP model preserves proximity and permutation in graph neural networks.

Paper proposes a new method for supervised manifold learning using random forest proximities.

Curvature regularization prevents distortion in graph embeddings.

Improved random forest proximities capture data geometry.

We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-preserving binary code. This code can be used to apply fast and memory-efficient approximation to nearest-neighbor searches. Our framework is …

EPINE enhances network embedding by improving adjacency matrix-based high-order proximity.

Graph embedding learns low-dimensional representations for nodes in a graph and effectively preserves the graph structure. Recently, a significant amount of progress has been made toward this emerging research area. However, there are several fundamental problems that remain open. First, existing methods fail to preser…

Complex embeddings handle non-metric proximity data better than traditional methods.

DE-PSGLD samples from constrained distributions in a decentralized manner.

EPNE models evolving network patterns for better predictions.

The paper tackles finding stationary points in stochastic convex optimization problems.

Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features. Existing studies only leverage network structure information and focus on preserving structural features. However, nodes in real-world networks often have a rich set of attributes providi…

This paper improves cross-domain learning using random forests for manifold alignment.

RFX accelerates and compresses Random Forests for large datasets.

DLC enhances distillation-based continual learning with lightweight plugins.

Federated learning approach for binary matrix factorization.

CTGCN learns dynamic graph embeddings preserving both local and global graph structure.

Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while th…

Paper proposes distributed optimization for federated learning with theoretical guarantees.

We develop a novel theoretical framework for understating OT schemes respecting a class structure. For this purpose, we propose a convex OT program with a sum-of-norms regularization term, which provably recovers the underlying class structure under geometric assumptions. Furthermore, we derive an accelerated proximal …

We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGRAPHEMB, is a general framework that provides a novel means to performing graph-level embedding in a comple…

Deep generative models have made tremendous advances in image and signal representation learning and generation. These models employ the full Euclidean space or a bounded subset as the latent space, whose flat geometry, however, is often too simplistic to meaningfully reflect the manifold structure of the data. In this…

Network embedding has become a hot research topic recently which can provide low-dimensional feature representations for many machine learning applications. Current work focuses on either (1) whether the embedding is designed as an unsupervised learning task by explicitly preserving the structural connectivity in the n…

Fix a finite set of points in Euclidean $n$-space $\euc^n$, thought of as a point-cloud sampling of a certain domain $D\subset\euc^n$. The Rips complex is a combinatorial simplicial complex based on proximity of neighbors that serves as an easily-computed but high-dimensional approximation to the homotopy type of $D$. …

We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an $ε$-suboptimal point in $\tilde{\mathcal{O}}(d(n + \sqrt{κd})\log(\frac{1}ε))$ FLOPS, where $n$ is number of samples, $d$ is feature dimension, and $κ$ is the condition number. As long as $n > d$, the proposed method…

ITSPACE improves covariance alignment faster than other methods.

In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization. Inexact proximal gradient methods are extremely important when exactly solving the proximal operator is time-consuming, or the proximal operator does not have an analytic sol…

Improves time series classification with forest proximities.

Improved sampling guarantees for weakly log-concave distributions.

Proves convergence of PSGLA for sampling non-convex potentials.

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackl…

Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations w…

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order proximity/adjacency matrix of the graph via computationally expen…

CFR-Pro enhances treatment effect estimation by incorporating local proximity.

Introduces PPMM algorithm for nonconvex robust regression problems.

Paper analyzes convergence of proximal algorithm in metric spaces without geodesic convexity.

Recently, tensor data (or multidimensional array) have been generated in many modern applications, such as functional magnetic resonance imaging (fMRI) in neuroscience and videos in video analysis. Many efforts are made in recent years to predict the relationship between tensor features and univariate responses. Howeve…

Extends RF proximities to all supervised distance-based machine learning contexts.

Improved bounds for proximal gradient algorithms with computational errors.

Paper extends theorem on covering spaces and Jordan curves.

Enhances graph embeddings by preserving graph topology.

Proximal algorithms applied to current deformation into cycles.

cGAP visualizes high-dimensional categorical data with interpretable geometric structure.

cGAP visualizes high-dimensional categorical data with interpretable geometric structure.

Innovative method solves nonconvex optimization on manifolds.

New PnP algorithm converges with relaxed proximal gradient descent.

This paper improves federated learning for industrial predictive analytics by accommodating client heterogeneity.