Minimal Learning Machine: A novel supervised distance-based approach for regression and classification

Souza, Amauri H.; Corona, Francesco; Barreto, Guilherme A.; Miché, Yoan; Lendasse, Amaury

doi:10.1016/j.neucom.2014.11.073

Cited by 56 publications

(39 citation statements)

References 26 publications

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“…Regarding human activity recognition approaches, most of the related published studies address such a recognition using supervised learning [18][19][20][21][22] or semisupervised learning [23,24]. Transfer learning has also been investigated, whereby the instances or models for activities in one domain can be transferred to improve the recognition accuracy in another domain for the purpose of reducing the need for training data [25][26][27].…”

Section: Activity Recognition-based Supervised Learningmentioning

confidence: 99%

Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Machot

Elkobaisi

Kyamakya

2020

Sensors

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Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life settings should cover a growing number of activities in various domains, whereby a significant part of instances will not be present in the training data set. However, to cover all possible activities in advance is a complex and expensive task. Concretely, we need a method that can extend the learning model to detect unseen activities without prior knowledge regarding sensor readings about those previously unseen activities. In this paper, we introduce an approach to leverage sensor data in discovering new unseen activities which were not present in the training set. We show that sensor readings can lead to promising results for zero-shot learning, whereby the necessary knowledge can be transferred from seen to unseen activities by using semantic similarity. The evaluation conducted on two data sets extracted from the well-known CASAS datasets show that the proposed zero-shot learning approach achieves a high performance in recognizing unseen (i.e., not present in the training dataset) new activities.

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Section: Activity Recognition-based Supervised Learningmentioning

confidence: 99%

Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Machot

Elkobaisi

Kyamakya

2020

Sensors

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“…The third phase is an optimization problem, based on the predicted output distances and the ground truth points. The third phase is the computationally most complex 9 .…”

Section: Minimal Learning Machinementioning

confidence: 99%

“…Some of the popular machine learning classification approaches utilises the neural networks, support vector machines, random forests or deep learning classification methods 6 . Because those methods can be complex and time-consuming, it is an interesting idea to introduce HS images to the relatively new classifier, which is an easy to implement and has had a promising results on performance and accuracy 9 .…”

Section: Introductionmentioning

confidence: 99%

“…Minimal learning machine 9 (MLM) is a supervised, distance-based machine learning classification method that utilizes mapping between input and and output distances. The input distance is a distance between the training set and its subset R. The output distance is corresponding distances between the label values of the training set X and subset R. The MLM classification model is a generalization of a nearest neighbour classifier 9 . This approach requires argument sorting for the distances between the input and output values and assigning of the closest label value from the ground truth labels.…”

Section: Introductionmentioning

confidence: 99%

“…The aim is to have a collection of data points which represents the geometry of each class. This approach differs from the original MLM approach of picking the training points randomly 9 . The objective is to minimize the size of R, as it is the most influential factor 9 in the computational complexity of this method, without reducing the accuracy with reduced training points.…”

Section: Introductionmentioning

confidence: 99%

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Minimal learning machine in hyperspectral imaging classification

Hakola

Pölönen

2020

Image and Signal Processing for Remote Sensing XXVI

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A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum is classified pixel-by-pixel. In some of the real-time applications, the amount of the HS image data causes performance challenges. Those issues relate to the platforms (e.g. drones) payload restrictions, the issues of the available energy and to the complexity of the machine learning models. In this study, we introduce the minimal learning machine (MLM) as a computationally cheap training and classification machine learning method for the hyperspectral imaging classification. MLM is a distance-based method that utilizes mapping between input and and output distances. Input distance is a distance between the training set and its subset R. Output distance is corresponding distances between the label values of the training set and the subset R. We propose a training point selection framework, which reduces the number of data points in the R by selecting the points class-by-class, in the direction of the principal components of each class. We test MLM's performance against four other classification machine learning methods: Random Forest, Artificial Neural Network, Support Vector Machine and Nearest Neighbours classifier with three known hyperspectral data sets. As the main outcomes, we will show how the performance is affected by the size of the subset R. We compare our subset selection method MLM's performance to the random selection MLM's performance. Results show that MLM is an computationally efficient way to train large training sets. MLM reduces the complexity of the analysis and provides computational benefits against other models. Proposed framework offers tools that can improve the MLM's classification time and the accuracy rate compared to the MLM with randomly picked training points.

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Statistical Similarity in Machine Learning

Klyushin

2023

Machine Learning Applications

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Minimal Learning Machine: A novel supervised distance-based approach for regression and classification

Cited by 56 publications

References 26 publications

Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Minimal learning machine in hyperspectral imaging classification

Statistical Similarity in Machine Learning

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