References of "Sidiropoulos, N.D."
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See detailSparse conjoint analysis through maximum likelihood estimation
Tsakonas, Efthymios; Jalden, J.; Sidiropoulos, N.D et al

in IEEE Transactions on Signal Processing (2013), 22

Conjoint analysis (CA) is a classical tool used in preference assessment, where the objective is to estimate the utility function of an individual, or a group of individuals, based on expressed preference ... [more ▼]

Conjoint analysis (CA) is a classical tool used in preference assessment, where the objective is to estimate the utility function of an individual, or a group of individuals, based on expressed preference data. An example is choice-based CA for consumer profiling, i.e., unveiling consumer utility functions based solely on choices between products. A statistical model for choice-based CA is investigated in this paper. Unlike recent classification-based approaches, a sparsity-aware Gaussian maximum likelihood (ML) formulation is proposed to estimate the model parameters. Drawing from related robust parsimonious modeling approaches, the model uses sparsity constraints to account for outliers and to detect the salient features that influence decisions. Contributions include conditions for statistical identifiability, derivation of the pertinent Cramér-Rao Lower Bound (CRLB), and ML consistency conditions for the proposed sparse nonlinear model. The proposed ML approach lends itself naturally to ℓ1-type convex relaxations which are well-suited for distributed implementation, based on the alternating direction method of multipliers (ADMM). A particular decomposition is advocated which bypasses the apparent need for outlier communication, thus maintaining scalability. The performance of the proposed ML approach is demonstrated by comparing against the associated CRLB and prior state-of-the-art using both synthetic and real data sets. [less ▲]

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See detailConnections between sparse estimation and robust statistical learning
Tsakonas, Efthymios; Jalden, J.; Sidiropoulos, N.D. et al

in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (2013)

Recent literature on robust statistical inference suggests that promising outlier rejection schemes can be based on accounting explicitly for sparse gross errors in the modeling, and then relying on ... [more ▼]

Recent literature on robust statistical inference suggests that promising outlier rejection schemes can be based on accounting explicitly for sparse gross errors in the modeling, and then relying on compressed sensing ideas to perform the outlier detection. In this paper, we consider two models for recovering a sparse signal from noisy measurements, possibly also contaminated with outliers. The models considered here are a linear regression model, and its natural one-bit counterpart where measurements are additionally quantized to a single bit. Our contributions can be summarized as follows: We start by providing conditions for identification and the Cramér-Rao Lower Bounds (CRLBs) for these two models. Then, focusing on the one-bit model, we derive conditions for consistency of the associated Maximum Likelihood estimator, and show the performance of relevant ℓ1-based relaxation strategies by comparing against the theoretical CRLB. [less ▲]

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