Semi-supervised time series classification method for quantum computing

AbstractIn this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quantum annealers and gate-model quantum processors. We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction. Using the solution to the reconstruction problem, we show how to extend this method to perform semi-supervised classification of TS data. We present results indicating our method is competitive with current semi- and unsupervised classification techniques, but using less data than classical techniques.

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Time series classification of radio signal strength for qualitative estimate of UAV motion

Machine Learning with Applications ◽

10.1016/j.mlwa.2021.100027 ◽

2021 ◽

pp. 100027

Author(s):

Samuel Teague ◽

Javaan Chahl

Keyword(s):

Time Series ◽

Radio Signal ◽

Signal Strength ◽

Time Series Classification ◽

Qualitative Estimate

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Detection of Yellow Hawkweed (Hieracium pratense) with High Resolution Multispectral Digital Imagery

Weed Technology ◽

10.1017/s0890037x0002371x ◽

1995 ◽

Vol 9 (3) ◽

pp. 477-483 ◽

Cited By ~ 34

Author(s):

Hubert W. Carson ◽

Lawrence W. Lass ◽

Robert H. Callihan

Keyword(s):

Supervised Classification ◽

Digital Images ◽

Ground Cover ◽

Unsupervised Classification ◽

Digital Imagery ◽

Charge Coupled Device ◽

Spectral Filters ◽

A Charge ◽

Northern Idaho

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

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Crop mapping by supervised classification of high resolution optical image time series using prior knowledge about crop rotation and topography

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS ◽

10.1109/igarss.2013.6723414 ◽

2013 ◽

Author(s):

Julien Osman ◽

Jordi Inglada ◽

Jean-Francois Dejoux ◽

Olivier Hagolle ◽

Gerard Dedieu

Keyword(s):

Time Series ◽

High Resolution ◽

Prior Knowledge ◽

Crop Rotation ◽

Supervised Classification ◽

Optical Image ◽

Crop Mapping

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Discriminate Supervised Weighted Scheme for the Classification of Time Series Signals

International Journal of Sociotechnology and Knowledge Development ◽

10.4018/ijskd.2021070101 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1-16

Author(s):

Elangovan Ramanujam ◽

S. Padmavathi

Keyword(s):

Time Series ◽

Time Series Data ◽

State Of The Art ◽

Statistical Significance ◽

Series Data ◽

Bag Of Words ◽

Time Series Classification ◽

Problem Of Time ◽

Weighted Matrix

Innovations and applicability of time series data mining techniques have significantly increased the researchers' interest in the problem of time series classification. Several algorithms have been proposed for this purpose categorized under shapelet, interval, motif, and whole series-based techniques. Among this, the bag-of-words technique, an extensive application of the text mining approach, performs well due to its simplicity and effectiveness. To extend the efficiency of the bag-of-words technique, this paper proposes a discriminate supervised weighted scheme to identify the characteristic and representative pattern of a class for efficient classification. This paper uses a modified weighted matrix that discriminates the representative and non-representative pattern which enables the interpretability in classification. Experimentation has been carried out to compare the performance of the proposed technique with state-of-the-art techniques in terms of accuracy and statistical significance.

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Few-Shot Time-Series Classification of Chemosensor Data

ECS Meeting Abstracts ◽

10.1149/ma2021-01541310mtgabs ◽

2021 ◽

Vol MA2021-01 (54) ◽

pp. 1310-1310

Author(s):

Bhargavi Mahesh ◽

Sebastian Hettenkofer ◽

Thorsten Graunke ◽

Jens-Uwe Garbas

Keyword(s):

Time Series ◽

Time Series Classification

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Overview of Hyperspectral Image Classification

Journal of Sensors ◽

10.1155/2020/4817234 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Wenjing Lv ◽

Xiaofei Wang

Keyword(s):

Supervised Classification ◽

Hyperspectral Image ◽

Unsupervised Classification ◽

Hyperspectral Images ◽

Hyperspectral Image Classification ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Good Classification ◽

Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

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