Item-Based Collaborative Filtering Based  on NLP Techniques

Kovenko, Volodymyr; Bogach, Ilona; Baraban, M. V.

doi:10.31649/1999-9941-2021-51-2-17-22

Cited by 2 publications

(2 citation statements)

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“…Amount of uncertainty (second moment) displayed by the VAE priors is lower. This is an expected phenomenon as vanilla VAEs are known to produce blurred and over-smoothed data due to compression; on the other hand, they perform well on denoising tasks [ 29 ], i.e. reconstructing underlying truth given corrupt data.…”

Section: Resultsmentioning

confidence: 99%

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

Semenova

Howes

et al. 2022

J. R. Soc. Interface.

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Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.

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Section: Resultsmentioning

confidence: 99%

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

Semenova

Howes

et al. 2022

J. R. Soc. Interface.

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“…The aim of the decoder is to rescale the encoder output to the initial shape of the data, as described by Kovenko et al [39]. The model is trained by using back-propagation.…”

Section: Autoencodermentioning

confidence: 99%

Generation and Optimization of Spectral Cluster Maps to Enable Data Fusion of CaSSIS and CRISM Datasets

et al. 2022

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Four-band color imaging of the Martian surface using the Color and Stereo Surface Imaging System (CaSSIS) onboard the European Space Agency’s ExoMars Trace Gas Orbiter exhibits a high color diversity in specific regions. Not only is the correlation of color diversity maps with local morphological properties desirable, but mineralogical interpretation of the observations is also of great interest. The relatively high spatial resolution of CaSSIS data mitigates its low spectral resolution. In this paper, we combine the broad-band imaging of the surface of Mars, acquired by CaSSIS with hyperspectral data from the Compact Reconnaissance Imaging Spectrometer (CRISM) onboard NASA’s Mars Reconnaissance Orbiter to achieve a fusion of both datasets. We achieve this using dimensionality reduction and data clustering of the high dimensional datasets from CRISM. In the presented research, CRISM data from the Coprates Chasma region of Mars are tested with different machine learning methods and compared for robustness. With the help of a suitable metric, the best method is selected and, in a further step, an optimal cluster number is determined. To validate the methods, the so-called “summary products” derived from the hyperspectral data are used to correlate each cluster with its mineralogical properties. We restrict the analysis to the visible range in order to match the generated clusters to the CaSSIS band information in the range of 436–1100 nm. In the machine learning community, the so-called UMAP method for dimensionality reduction has recently gained attention because of its speed compared to the already established t-SNE. The results of this analysis also show that this method in combination with the simple K-Means outperforms comparable methods in its efficiency and speed. The cluster size obtained is between three and six clusters. Correlating the spectral cluster maps with the given summary products from CRISM shows that four bands, and especially the NIR bands and VIS albedo, are sufficient to discriminate most of these clusters. This demonstrates that features in the four-band CaSSIS images can provide robust mineralogical information, despite the limited spectral information using semi-automatic processing.

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Item-Based Collaborative Filtering Based on NLP Techniques

Cited by 2 publications

References 0 publications

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

Generation and Optimization of Spectral Cluster Maps to Enable Data Fusion of CaSSIS and CRISM Datasets

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