2 research outputs found
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning
In the domain of machine learning algorithms, the significance of the loss
function is paramount, especially in supervised learning tasks. It serves as a
fundamental pillar that profoundly influences the behavior and efficacy of
supervised learning algorithms. Traditional loss functions, while widely used,
often struggle to handle noisy and high-dimensional data, impede model
interpretability, and lead to slow convergence during training. In this paper,
we address the aforementioned constraints by proposing a novel robust, bounded,
sparse, and smooth (RoBoSS) loss function for supervised learning. Further, we
incorporate the RoBoSS loss function within the framework of support vector
machine (SVM) and introduce a new robust algorithm named
-SVM. For the theoretical analysis, the
classification-calibrated property and generalization ability are also
presented. These investigations are crucial for gaining deeper insights into
the performance of the RoBoSS loss function in the classification tasks and its
potential to generalize well to unseen data. To empirically demonstrate the
effectiveness of the proposed -SVM, we evaluate it on
real-world UCI and KEEL datasets from diverse domains. Additionally, to
exemplify the effectiveness of the proposed -SVM within the
biomedical realm, we evaluated it on two medical datasets: the
electroencephalogram (EEG) signal dataset and the breast cancer (BreaKHis)
dataset. The numerical results substantiate the superiority of the proposed
-SVM model, both in terms of its remarkable generalization
performance and its efficiency in training time
Support matrix machine: A review
Support vector machine (SVM) is one of the most studied paradigms in the
realm of machine learning for classification and regression problems. It relies
on vectorized input data. However, a significant portion of the real-world data
exists in matrix format, which is given as input to SVM by reshaping the
matrices into vectors. The process of reshaping disrupts the spatial
correlations inherent in the matrix data. Also, converting matrices into
vectors results in input data with a high dimensionality, which introduces
significant computational complexity. To overcome these issues in classifying
matrix input data, support matrix machine (SMM) is proposed. It represents one
of the emerging methodologies tailored for handling matrix input data. The SMM
method preserves the structural information of the matrix data by using the
spectral elastic net property which is a combination of the nuclear norm and
Frobenius norm. This article provides the first in-depth analysis of the
development of the SMM model, which can be used as a thorough summary by both
novices and experts. We discuss numerous SMM variants, such as robust, sparse,
class imbalance, and multi-class classification models. We also analyze the
applications of the SMM model and conclude the article by outlining potential
future research avenues and possibilities that may motivate academics to
advance the SMM algorithm