Gaussian process regression for out-of-sample extension

Barkan, Oren; Weill, Jonathan; Averbuch, Amir

doi:10.1109/mlsp.2016.7738832

Cited by 9 publications

(8 citation statements)

References 7 publications

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“…This computation can be seen as a weighted nearest neighbor approach, where α controls the neighborhood size. The proposed method further shares some similarity to previous out-ofsample extension methodologies [19,20,21]. As we shell see in Sec.…”

Section: Cb2cf-nn: Cb2cf Meets the Neighborhoodsupporting

confidence: 63%

Cold Start Revisited: A Deep Hybrid Recommender with Cold-Warm Item Harmonization

Barkan

Hirsch

Katz

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Collaborative filtering-based recommender systems are known to suffer from the item cold-start problem. Most recent attempts to mitigate this problem presented parametric approaches, such as deep content based models. In this paper, we show that a straightforward application of parametric models may lead to discrepancies between the cold and warm items' distributions in the CF space. As a remedy, we propose to combine parametric with non-parametric estimation for robust cold item placement. Extensive evaluation indicates that our method is competitive with other baselines, while producing cold items placement that better resembles the distribution of warm items in the collaborative filtering space.

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Section: Cb2cf-nn: Cb2cf Meets the Neighborhoodsupporting

confidence: 63%

Cold Start Revisited: A Deep Hybrid Recommender with Cold-Warm Item Harmonization

Barkan

Hirsch

Katz

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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show abstract

“…The intuition to use GPR for detecting concept drift is novel even though the Bayesian non-parametric approach [18], primarily intended for anomaly detection, comes close to our work in a single manifold setting. However, their choice of the Euclidean distance (in original R D space) based kernel for its covariance matrix, can result in high Procrustes error, as shown in 4.…”

Section: Related Workmentioning

confidence: 80%

Learning Manifolds from Non-stationary Streams

Mahapatra

Chandola

2021

Preprint

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Streaming adaptations of manifold learning based dimensionality reduction methods, such as Isomap, are based on the assumption that a small initial batch of observations is enough for exact learning of the manifold, while remaining streaming data instances can be cheaply mapped to this manifold. However, there are no theoretical results to show that this core assumption is valid. Moreover, such methods typically assume that the underlying data distribution is stationary and are not equipped to detect, or handle, sudden changes or gradual drifts in the distribution that may occur when the data is streaming. We present theoretical results to show that the quality of a manifold asymptotically converges as the size of data increases. We then show that a Gaussian Process Regression (GPR) model, that uses a manifold-specific kernel function and is trained on an initial batch of sufficient size, can closely approximate the state-of-art streaming Isomap algorithms. The predictive variance obtained from the GPR prediction is then shown to be an effective detector of changes in the underlying data distribution. Results on several synthetic and real data sets show that the resulting algorithm can effectively learn lower dimensional representation of high dimensional data in a streaming setting, while identifying shifts in the generative distribution.

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“…The similarity of the two approaches-particularly, the method of Kriging and Geometric Harmonics-has been noted, both in the original paper by Coifman et al [4] and by others [8]. These authors mostly focused on the noise-free interpretation of GH.…”

Section: Introductionmentioning

confidence: 96%

On the Correspondence between Gaussian Processes and Geometric Harmonics

Dietrich,

Bello-Rivas,

Kevrekidis

2021

Preprint

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We discuss the correspondence between Gaussian process regression and Geometric Harmonics, two similar kernel-based methods that are typically used in different contexts. Research communities surrounding the two concepts often pursue different goals. Results from both camps can be successfully combined, providing alternative interpretations of uncertainty in terms of error estimation, or leading towards accelerated Bayesian Optimization due to dimensionality reduction.

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Gaussian process regression for out-of-sample extension

Cited by 9 publications

References 7 publications

Cold Start Revisited: A Deep Hybrid Recommender with Cold-Warm Item Harmonization

Cold Start Revisited: A Deep Hybrid Recommender with Cold-Warm Item Harmonization

Learning Manifolds from Non-stationary Streams

On the Correspondence between Gaussian Processes and Geometric Harmonics

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