Quantum algorithms present unique advantages over classical methods but remain constrained by the limited number of qubits in current quantum computers.This limitation hinders their effectiveness in machine learning tasks, such as image classification.Despite its relevance, the impact of these constraints on quantum machine learning remains underexplored.This study addresses this gap by analyzing preprocessing techniques for preparing images on quantum processors.We evaluated 10 dimensionality reduction methods across four standard datasets using three distinct quantum neural network architectures.The results provide valuable insights into optimizing classification efficiency under qubit constraints, paving the way for broader applications of quantum machine learning.

While large margin classifiers are originally an outcome of an optimization framework, support vectors (SVs) can be obtained from geometric approaches. This article presents advances in the use of Gabriel graphs (GGs) in binary and multiclass classification problems. For Chipclass, a hyperparameterless and optimization-less GG-based binary classifier, we discuss how activation functions and support edge (SE)-centered neurons affect the classification, proposing smoother functions and structural SV (SSV)-centered neurons to achieve margins with low probabilities and smoother classification contours. We extend the neural network architecture, which can be trained with backpropagation with a softmax function and a cross-entropy loss, or by solving a system of linear equations. A new subgraph-/distance-based membership function for graph regularization is also proposed, along with a new GG recomputation algorithm that is less computationally expensive than the standard approach. Experimental results with the Friedman test show that our method was better than previous GG-based classifiers and statistically equivalent to tree-based models.

In order to solve non-linear problems, kernel-based classifiers rely on implicit mappings to very high-dimensional spaces.These target spaces, although mathematically robust, often lack the property of visual interpretation, limiting the intuition of the problem at hand.In this work, the notion of a similarity space is presented, to which one can map input samples and visualize how they interact under a given kernel function.By exploring statistics in such space, a class separability measure is derived, which can be used to find optimal kernel parameters for binary classification.Experiments using support vector machines were conducted, showing the method's effectiveness when compared to grid-search approaches.

Although it requires simple computations, provides good performance on linear classification tasks and offers a suitable environment for active learning strategies, the Hebbian learning rule is very sensitive to how the training data relate to each other in the input space. Since this spatial arrangement is inherent to each set of samples, the practical application of this learning paradigm is limited. Thus, representation learning may play an important role in projecting the input data into a new space where linear separability is improved. Earlier methods based on orthogonal coding addressed this issue but presented many side effects, impoverishing the generalization of the model. Hence, this paper considers a recently proposed method based on kernel density estimators, which performs a likelihood-based projection where linear separability and generalization capacity are enhanced in an autonomous fashion. Results show that this novel method allows one to use linear classifiers to solve many binary classification problems and overcome the performance of well-established classifiers.

The number of connected embedded edge computing Internet of Things (IoT) devices has been increasing over the years, contributing to the significant growth of available data in different scenarios. Thereby, machine learning algorithms arise to enable task automation and process optimization based on those data. However, due to some learning methods' computational complexity implementing geometric classifiers, it is a challenge to map these on embedded systems or devices with limited resources in size, processing, memory, and power, to accomplish the desired requirements. This hampers the applicability of these methods to complex industrial embedded edge applications. This work evaluates strategies to reduce classifiers' implementation costs based on the CHIP-clas model, independent of hyperparameter tuning and optimization algorithms. The proposal aims to evaluate the tradeoff between numerical precision and model performance and analyze the hardware implementations of a distance-based classifier. Two 16 -b floating-point formats were compared to the 32 -b floating-point precision implementation. Also, a new hardware architecture was developed and then compared to the state-of-the-art reference. The results indicate that the model is robust to low precision computation, providing statistically equivalent results compared to the baseline model, also pointing out statistically equivalent performance and a global speed-up factor of approx 4.39 in processing time.

It is well known that there is an increasing interest in edge computing to reduce the distance between cloud and end devices, especially for machine learning (ML) methods. However, when related to latency-sensitive applications, little work can be found in ML literature on suitable embedded systems implementations. This article presents new ways to implement the decision rule of a large margin classifier based on Gabriel graphs as well as an efficient implementation of this on an embedded system. The proposed approach uses the nearest neighbor method as the decision rule, and the implementation starts from an RTL pipeline architecture developed for binary large margin classifiers and proposes the integration in a hardware/software co-design. Results showed that the proposed approach was statistically similar to the classifier and had a speedup factor of up to eight times compared to the classifier executed in software, with performance suitable for ML latency-sensitive applications.

This brief presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the data set from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometrical support vectors (SVs) (analogous to support vector machines (SVMs) SVs) are obtained in order to yield the final large margin solution from a Gaussian mixture model. Experiments with 20 data sets have shown that the solutions obtained with the proposed method are statistically equivalent to those obtained with SVMs. However, the present method does not require optimization and can also be extended to large data sets using the cascade SVM concept.

The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction.

It is not uncommon for data obtained from systems to change after the model is learned. These occurrences are named dataset shifts and to deal with them models with the ability to adapt to data changes must be used. A strategy that can be easily integrated to other classifiers is proposed. It creates a geometrical representation of data that extracts information from both labelled and unlabelled data. Then data entropy and Jensen-Shannon dissimilarity tests are used during the model selection to handle cases where data shift. Results have shown that the proposed method is promising because of its simple integration with state of art classifiers and its performance in enhancing said classifiers accuracy in the studied cases.

This paper presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the dataset from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometric vectors, analogous to SVM's support vectors are obtained in order to yield the final large margin solution from a mixture model approach. A preliminary experimental study with five real-world benchmarks showed that the method is promising.