Online EM Algorithm

Online Bagging and Boosting: Enhancing Machine Learning Models for Imbalanced Data and Robust Visual Tracking Online Bagging and Boosting are ensemble learning techniques that improve the performance of machine learning models by combining multiple weak learners into a strong learner. These methods have been applied to various domains, including imbalanced data streams and visual tracking, to address challenges such as data imbalance, drifting, and model complexity. Imbalanced data streams are a common issue in machine learning, where the distribution of classes is uneven. Online Ensemble Learning for Imbalanced Data Streams (Wang & Pineau, 2013) proposes a framework that fuses online ensemble algorithms with cost-sensitive bagging and boosting techniques. This approach bridges two research areas and provides a set of online cost-sensitive algorithms with guaranteed convergence under certain conditions. In the field of visual tracking, Multiple Instance Learning (MIL) has been used to alleviate the drifting problem. Instance Significance Guided Multiple Instance Boosting for Robust Visual Tracking (Liu, Lu, & Zhou, 2020) extends this idea by incorporating instance significance estimation into the online MILBoost framework. This method outperforms existing MIL-based and boosting-based trackers in experiments with challenging public datasets. Recent research has also explored the combination of bagging and boosting techniques in various contexts. A Bagging and Boosting Based Convexly Combined Optimum Mixture Probabilistic Model (Adnan & Mahmud, 2021) suggests a model that iteratively searches for the optimum probabilistic model, providing the maximum p-value. FedGBF (Han, Du, & Yang, 2022) is a novel vertical federated learning framework that integrates the advantages of boosting and bagging by building decision trees in parallel as a base learner for boosting. Practical applications of online bagging and boosting include: 1. Imbalanced data classification: Online ensemble learning techniques can effectively handle imbalanced data streams, improving classification performance in domains such as fraud detection and medical diagnosis. 2. Visual tracking: Instance significance guided boosting can enhance the performance of visual tracking systems, benefiting applications like surveillance, robotics, and autonomous vehicles. 3. Federated learning: Combining bagging and boosting in federated learning settings can lead to more efficient and accurate models, which are crucial for privacy-preserving applications in industries like healthcare and finance. A company case study that demonstrates the effectiveness of these techniques is the application of Interventional Bag Multi-Instance Learning (IBMIL) on whole-slide pathological images (Lin et al., 2023). IBMIL is a novel scheme that achieves deconfounded bag-level prediction, suppressing the bias caused by bag contextual prior. This method has been shown to consistently boost the performance of existing MIL methods, achieving state-of-the-art results in whole-slide pathological image classification. In conclusion, online bagging and boosting techniques have demonstrated their potential in addressing various challenges in machine learning, such as imbalanced data, drifting, and model complexity. By combining the strengths of multiple weak learners, these methods can enhance the performance of machine learning models and provide practical solutions for a wide range of applications.

Online K-Means is a machine learning technique that efficiently clusters data points in real-time as they arrive, providing a scalable solution for large-scale data analysis. Online K-Means clustering is a powerful machine learning method that extends the traditional K-Means algorithm to handle data streams. In this setting, the algorithm receives data points one by one and assigns them to a cluster before receiving the next data point. This online approach allows for efficient processing of large-scale datasets, making it particularly useful in applications where data is continuously generated or updated. Recent research in online K-Means has focused on improving the algorithm's performance and scalability. For example, one study proposed an algorithm that achieves competitive clustering results while operating in a more constrained computational model. Another study analyzed the convergence rate of stochastic K-Means variants, showing that they converge towards local optima at a rate of O(1/t) under general conditions. These advancements have made online K-Means more robust and applicable to a wider range of problems. However, there are still challenges and complexities in online K-Means clustering. One issue is the impact of the ordering of the dataset and whether the number of data points is known in advance. Researchers have explored different cases and provided upper and lower bounds for the number of centers needed to achieve a constant approximation in various settings. Another challenge is the memory efficiency of episodic control reinforcement learning, where researchers have proposed a dynamic online K-Means algorithm that significantly improves performance at smaller memory sizes. Practical applications of online K-Means clustering can be found in various domains. For instance, it has been used for detecting overlapping communities in large benchmark graphs, providing a faster and more accurate solution compared to existing methods. In fraud detection, a scalable and sparsity-aware privacy-preserving K-Means clustering framework has been proposed, which achieves competitive performance in terms of running time and communication size, especially on sparse datasets. Additionally, online K-Means has been applied to unsupervised visual representation learning, where a novel clustering-based pretext task with online constrained K-Means has been shown to achieve competitive performance. One company case study involves the use of online K-Means in video panoptic segmentation, a task that aims to achieve comprehensive pixel-level scene understanding by segmenting all pixels and associating objects in a video. Researchers have proposed a unified approach called Video-kMaX, which consists of a within clip segmenter and a cross-clip associater. This approach sets a new state-of-the-art on various benchmarks for video panoptic segmentation and video semantic segmentation. In conclusion, online K-Means clustering is a versatile and efficient machine learning technique that has been successfully applied to various real-world problems. By addressing the challenges and complexities of this method, researchers continue to improve its performance and applicability, making it an essential tool for large-scale data analysis and real-time decision-making.