6 accepted papers
Graphs are a prevalent tool in data science, as they model the inherent structure of the data. Typically they are constructed either by connecting nearest samples, or by learning them from data, solving an optimization problem. While graph learning does achieve a better quality, it also comes with a…
We consider the problem of inferring the hidden structure of high-dimensional time-varying data. In particular, we aim at capturing the dynamic relationships by representing data as valued nodes in a sequence of graphs. Our approach is motivated by the observation that imposing a meaningful graph to…
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 learn the edge weights w∈\mathbbR_+^m(m-1)/2 under the smoothness assumption that \rm trX^⊤LX is small, where L is the graph La…
Mining useful clusters from high dimensional data has received significant attention of the signal processing and machine learning community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such m…
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and son…
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first issue with a sparse penalty term. The second is…