8 accepted papers
Matrix Completion (MC) i.e., estimating the missing values of an unknown matrix based on limited information, has been a prominent topic of study in the last decades. In this paper, we focus more precisely on a little-known aspect of MC, namely how the recommendation of new entries can help improve…
We study online robust matrix completion on graphs. At each iteration a vector with some entries missing is revealed and our goal is to reconstruct it by identifying the underlying low-dimensional subspace from which the vectors are drawn. We assume there is an underlying graph structure to the data…
This work presents a distributed algorithm for nonlinear adaptive learning. In particular, a set of nodes obtain measurements, sequentially one per time step, which are related via a nonlinear function; their goal is to collectively minimize a cost function by employing a diffusion based Kernel Leas…
Accurate coverage maps are an important tool for network planning and operation but it is often impossible to obtain these maps completely from measurements. In this paper we describe two new methods that enable operators to minimize the cost for obtaining a complete coverage map at high accuracy. O…
This paper proposes a novel algorithm for online distributed dictionary learning, where a set of nodes is requested to collectively estimate a common dictionary via sequentially received data vectors. At each time instance, in which a new datum becomes available, the sparse representation of the dat…
A novel algorithm for distributed labeling of speech sources is proposed. We consider a wireless sensor network comprising devices that are equipped with multiple microphones, which can “hear” a number of speech signals. The labeling task is performed in a decentralized fashion with a new two-step a…
A promising approach when dealing with massive data sets is to apply randomized dimensionality reduction and then operate in lower dimensions. This paper deals with the randomized linear regression task in the case where the available data are sporadically corrupted. Instead of relying to minimizati…
This paper presents pattern classification to a predefined set of classes as a missing data task. This is achieved by first augmenting the feature vector of each training pattern with the corresponding binary codeword representing its class. A Restricted Boltzmann Machine (RBM) or a Dictionary Learn…