How to learn a graph from smooth signals
V Kalofolias - Artificial intelligence and statistics, 2016 - proceedings.mlr.press
We propose a framework to learn the graph structure underlying a set of smooth signals.
Given X∈\mathbbR^ m\times n whose rows reside on the vertices of an unknown graph, we …
Given X∈\mathbbR^ m\times n whose rows reside on the vertices of an unknown graph, we …
Laplacian regularized low-rank representation and its applications
Low-rank representation (LRR) has recently attracted a great deal of attention due to its
pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a …
pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a …
Heterogeneous domain adaptation through progressive alignment
In real-world transfer learning tasks, especially in cross-modal applications, the source
domain and the target domain often have different features and distributions, which are well …
domain and the target domain often have different features and distributions, which are well …
Robust joint graph sparse coding for unsupervised spectral feature selection
In this paper, we propose a new unsupervised spectral feature selection model by
embedding a graph regularizer into the framework of joint sparse regression for preserving …
embedding a graph regularizer into the framework of joint sparse regression for preserving …
Unsupervised cross-dataset transfer learning for person re-identification
P Peng, T ** of hyperspectral images
Hyperspectral image destri** is a challenging and promising theme in remote sensing.
Stri** noise is a ubiquitous phenomenon in hyperspectral imagery, which may severely …
Stri** noise is a ubiquitous phenomenon in hyperspectral imagery, which may severely …