A review of different dimensionality reduction methods for the prediction of sugar content from hyperspectral images of wine grape berries

2021 ◽  
Vol 113 ◽  
pp. 107889
Author(s):  
Rui Silva ◽  
Pedro Melo-Pinto
Keyword(s):  
Dimensionality Reduction ◽  
Sugar Content ◽  
Hyperspectral Images ◽  
Wine Grape ◽  
Grape Berries ◽  
Reduction Methods

scholarly journals Investigating the Effects of a Combined Spatial and Spectral Dimensionality Reduction Approach for Aerial Hyperspectral Target Detection Applications

2021 ◽  
Vol 13 (9) ◽  
pp. 1647
Author(s):  
Fraser Macfarlane ◽  
Paul Murray ◽  
Stephen Marshall ◽  
Henry White
Keyword(s):  
Dimensionality Reduction ◽  
Target Detection ◽  
Effective Means ◽  
Reduction Process ◽  
Hyperspectral Images ◽  
Detection Algorithms ◽  
Wide Range ◽  
Reduction Methods ◽  
Spectral Dimensionality ◽  
Spatial Redundancy

Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

scholarly journals Supervised dimensionality reduction for big data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua T. Vogelstein ◽  
Eric W. Bridgeford ◽  
Minh Tang ◽  
Da Zheng ◽  
Christopher Douville ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Data Science ◽  
Real Data ◽  
Low Rank ◽  
Conditional Moment ◽  
Desktop Computer ◽  
Reduction Techniques ◽  
Reduction Methods ◽  
The Individual ◽  
Low Dimensional

AbstractTo solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

Automatic configuration of spectral dimensionality reduction methods

2010 ◽  
Vol 31 (12) ◽  
pp. 1720-1727 ◽  
Author(s):  
Michał Lewandowski ◽  
Dimitrios Makris ◽  
Jean-Christophe Nebel
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Methods ◽  
Spectral Dimensionality

Brix, pH and anthocyanin content determination in whole Port wine grape berries by hyperspectral imaging and neural networks

2015 ◽  
Vol 115 ◽  
pp. 88-96 ◽  
Author(s):  
Armando M. Fernandes ◽  
Camilo Franco ◽  
Ana Mendes-Ferreira ◽  
Arlete Mendes-Faia ◽  
Pedro Leal da Costa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Anthocyanin Content ◽  
Wine Grape ◽  
Port Wine ◽  
Grape Berries ◽  
Content Determination
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