403 research outputs found
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
Energy Efficient Smartphone-Based Activity Recognition Using Fixed-Point Arithmetic
In this paper we propose a novel energy efficient approach for the recog- nition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre- serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method
Energy efficient smartphone-based activity recognition using fixed-point arithmetic
In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting
assisted living applications such as remote patient activity monitoring for the disabled
and the elderly. The method exploits fixed-point arithmetic to propose a modified
multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre-
serve the smartphone battery lifetime with respect to the conventional floating-point
based formulation while maintaining comparable system accuracy levels. Experiments
show comparative results between this approach and the traditional SVM in terms of
recognition performance and battery consumption, highlighting the advantages of the
proposed method.Peer ReviewedPostprint (published version
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