Linear Discriminant Analysis

Izenman, Alan Julian

doi:10.1007/978-0-387-78189-1_8

Cited by 388 publications

(181 citation statements)

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“…These results follow the same procedure as explained in Section 3.3, but with different t-SNE input parameters and input spectral ranges. The motivation for this approach is to search for stars in their early evolution phases (Žerjal et al 2013) for which features in Hα, Hβ, and 7 Li spectral lines can be diagnostic of their activity (Soderblom 2010;Jeffries 2014). Perplexity is set to 50, and the spectral ranges 4841-4881 Å(Hβ) and 6543-6583 Å(Hα) are selected for the first t-SNE projection of the whole working set, while a perplexity of 15 and reduced spectral ranges (4859-4863, 6561-6565, and 6706-6710 Å) around the three diagnostic lines are selected for the second t-SNE projection of the filtered working set.…”

Section: Specific Search For Young/active Starsmentioning

confidence: 99%

The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

Traven

Matijevič

Zwitter

et al. 2017

ApJS

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Galah is an ongoing high-resolution spectroscopic survey with the goal of disentangling the formation history of the Milky Way using the fossil remnants of disrupted star formation sites that are now dispersed around the Galaxy. It is targeting a randomly selected magnitude-limited (V 14) sample of stars, with the goal of observing one million objects. To date, 300,000 spectra have been obtained. Not all of them are correctly processed by parameter estimation pipelines, and we need to know about them. We present a semi-automated classification scheme that identifies different types of peculiar spectral morphologies in an effort to discover and flag potentially problematic spectra and thus help to preserve the integrity of the survey results. To this end, we employ the recently developed dimensionality reduction technique t-SNE (t-distributed stochastic neighbor embedding), which enables us to represent the complex spectral morphology in a two-dimensional projection map while still preserving the properties of the local neighborhoods of spectra. We find that the majority (178,483) of the 209,533 Galah spectra considered in this study represents normal single stars, whereas 31,050 peculiar and problematic spectra with very diverse spectral features pertaining to 28,579 stars are distributed into 10 classification categories: hot stars, cool metal-poor giants, molecular absorption bands, binary stars, Hα/Hβ emission, Hα/Hβ emission superimposed on absorption, Hα/Hβ P-Cygni, Hα/Hβ inverted P-Cygni, lithium absorption, and problematic. Classified spectra with supplementary information are presented in the catalog, indicating candidates for follow-up observations and population studies of the short-lived phases of stellar evolution.

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Section: Specific Search For Young/active Starsmentioning

confidence: 99%

The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

Traven

Matijevič

Zwitter

et al. 2017

ApJS

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“…7. Test data is evaluated via a multiclass-trained linear discriminant analysis classifier (LDA) for both types of features [49].…”

Section: Recognitionmentioning

confidence: 99%

Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach

et al. 2018

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Abstract:In current clinical practice, functional limitations due to chronic musculoskeletal diseases are still being assessed subjectively, e.g., using questionnaires and function scores. Performance-based methods, on the other hand, offer objective insights. Hence, they recently attracted more interest as an additional source of information. This work offers a step towards the shift to performance-based methods by recognizing standardized activities from continuous readings using a single accelerometer mounted on a patient's arm. The proposed procedure consists of two steps. Firstly, activities are segmented, including rejection of non-informative segments. Secondly, the segments are associated to predefined activities using a multiway pattern matching approach based on higher order discriminant analysis (HODA). The two steps are combined into a multi-layered framework. Experiments on data recorded from 39 patients with spondyloarthritis show results with a classification accuracy of 94.34% when perfect segmentation is assumed. Automatic segmentation has 89.32% overlap with this ideal scenario. However, combining both drops performance to 62.34% due to several badly-recognized subjects. Still, these results are shown to significantly outperform a more traditional pattern matching approach. Overall, the work indicates promising viability of the technique to automate recognition and, through future work, assessment, of functional capacity.

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“…None of the work has been carried out in LDA on service selection. The LDA is used for service discovery by reducing the dimension of service data and ignores the inequality of local data points of a similar class using matrix representation and calculates the overall score of QoS for each service, then ranks the services with the highest QoS values Izenman (2013). The intention of linear discriminant analysis is to detect a class to which services should belong to the closest mean.…”

Section: Introductionmentioning

confidence: 99%

A Four-level Linear Discriminant Analysis Based Service Selection in the Cloud Environment

Bhushan¹,

Reddy²

2016

IJTech

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The cloud is an outstanding platform to deal with functionally equivalent services which are exponentially increasing day-by-day. The selection of services to meet the client requirements is a subtle task. The services can be selected by ranking all the candidate services using their network and non-network Quality-of-Service (QoS) parameters, which is formulated as a NP hard optimization problem. In this paper, we proposed a linear discriminant analysis (LDA) based a four level matching model for service selection based on QoS parameters, which includes description matching of a service, matchmaking phase, LDA-based QoS matching and ranking. The LDA-service selection agent is deployed on each cloud to classify services into classes and rank the services based on the aggregate QoS value of each service. Finally, the test results show the efficiency in service selection with minimal discovery overhead, significant reduction in the computation time and the number of candidate services to be considered.

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Linear Discriminant Analysis

Cited by 388 publications

References 0 publications

The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach

A Four-level Linear Discriminant Analysis Based Service Selection in the Cloud Environment

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