Nicholas Arcolano

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Covariance matrix estimates are an essential part of many signal processing algorithms, and are often used to determine a lowdimensional principal subspace via their spectral decomposition. However, for sufficiently high-dimensional matrices exact eigenanalysis is computationally intractable, and in the case of limited data, sample eigenvalues and(More)
In many applications, it is convenient to represent data as a graph, and often these datasets will be quite large. This paper presents an architecture for analyzing massive graphs, with a focus on signal processing applications such as modeling, filtering, and signal detection. We describe the architecture, which covers the entire processing chain, from(More)
As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non-Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must have access to flexible and tractable statistical models. One model currently in use is the Chung-Lu random(More)
When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For(More)
When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For(More)
Recent work on signal detection in graph-based data focuses on classical detection when the signal and noise are both in the form of discrete entities and their relationships. In practice, the relationships of interest may not be directly observable, or may be observed through a noisy mechanism. The effects of imperfect observations add another layer of(More)
Anomaly detection in massive networks has numerous theoretical and computational challenges, especially as the behavior to be detected becomes small in comparison to the larger network. This presentation focuses on recent results in three key technical areas, specifically geared toward spectral methods for detection. We first discuss recent models for(More)
Covariance matrix estimates are an essential part of many signal processing algorithms, and are often used to determine a low-dimensional principal subspace via their spectral decomposition. However, for sufficiently high-dimensional matrices exact eigen-analysis is computationally intractable, and in the case of limited data, sample eigenvalues and(More)