Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledg

Representing 3D shape in sparse range images for urban object classification

This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing

Representing 3D shape in sparse range images for urban object classification

This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing

Coresets for Clustering: Foundations and Challenges

Clustering is a fundamental task in machine learning and data analysis. The main challenge for clustering in big data sets is that classical clustering algorithms often do not scale well. Coresets are data reduction techniques that turn big data into a tiny proxy. Prior research has shown that coresets can provide a scalable solution to clustering problems and imply streaming and distributed algorithms. In this work, we aim to solve a fundamental question and two modern challenges in coresets for clustering. \textsf{Beyond Euclidean Space}: Coresets for Clustering in Euclidean space have been well studied and coresets of constant size are known to exist. While very few results are known beyond Euclidean space. It becomes a fundamental problem that what kind of metric space admits constant-sized coresets for clustering. We focus on graph metrics which is a common ambient space for clustering. We provide positive results that assert constant-sized coresets exist in various families of graph metrics including graphs of bounded treewidth, planar graphs and the more general excluded-minor graphs. \textsf{Missing Value}: Missing value is a common phenomenon in real data sets. Clustering under the existence of missing values is a very challenging task. In this work, we construct the first coresets for clustering with multiple missing values. Previously, such coresets were only known to exist when each data point has at most one missing value \cite{DBLP:conf/nips/MaromF19}. We further design a near-linear time algorithm to construct our coresets. This algorithm implies the first near-linear time approximation scheme for \kMeans clustering with missing values and improves a recent result by \cite{DBLP:conf/soda/EibenFGLPS21}. \textsf{Simultaneous Coresets}: Most classical coresets are limited to a specific clustering objective. When there are multiple potential objectives, a stronger notion of “simultaneous coresets” is needed. Simultaneous coresets provide the approximations for a family of objectives and can serve as a more flexible data reduction tool. In this work, we design the first simultaneous coresets for a large clustering family which includes both \kMedian and \kCenter