Fast design of reduced complexity nearest-neighbor classifiers using triangular inequality

In this paper, we propose a method of designing a reduced complexity nearest-neighbor (RCNN) classifier with near-minimal computational complexity from a given nearest-neighbor classifier that has high input dimensionality and a large number of class vectors. We applied our method to the classification problem of handwritten numerals in the NIST database. If the complexity of the RCNN classifier is normalized to that of the given classifier, the complexity of the derived classifier is 62 percent, 2 percent higher than that of the optimal classifier. This was found using the exhaustive search.Institute of Information Technology Assessment (IITA), Korea, under research grant 96060-IT2-I2

Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs

Shapelets are discriminative time series subsequences that allow generation of interpretable classification models, which provide faster and generally better classification than the nearest neighbor approach. However, the shapelet discovery process requires the evaluation of all possible subsequences of all time series in the training set, making it extremely computation intensive. Consequently, shapelet discovery for large time series datasets quickly becomes intractable. A number of improvements have been proposed to reduce the training time. These techniques use approximation or discretization and often lead to reduced classification accuracy compared to the exact method. We are proposing the use of ensembles of shapelet-based classifiers obtained using random sampling of the shapelet candidates. Using random sampling reduces the number of evaluated candidates and consequently the required computational cost, while the classification accuracy of the resulting models is also not significantly different than that of the exact algorithm. The combination of randomized classifiers rectifies the inaccuracies of individual models because of the diversity of the solutions. Based on the experiments performed, it is shown that the proposed approach of using an ensemble of inexpensive classifiers provides better classification accuracy compared to the exact method at a significantly lesser computational cost

Learning in the Real World: Constraints on Cost, Space, and Privacy

The sheer demand for machine learning in fields as varied as: healthcare, web-search ranking, factory automation, collision prediction, spam filtering, and many others, frequently outpaces the intended use-case of machine learning models. In fact, a growing number of companies hire machine learning researchers to rectify this very problem: to tailor and/or design new state-of-the-art models to the setting at hand. However, we can generalize a large set of the machine learning problems encountered in practical settings into three categories: cost, space, and privacy. The first category (cost) considers problems that need to balance the accuracy of a machine learning model with the cost required to evaluate it. These include problems in web-search, where results need to be delivered to a user in under a second and be as accurate as possible. The second category (space) collects problems that require running machine learning algorithms on low-memory computing devices. For instance, in search-and-rescue operations we may opt to use many small unmanned aerial vehicles (UAVs) equipped with machine learning algorithms for object detection to find a desired search target. These algorithms should be small to fit within the physical memory limits of the UAV (and be energy efficient) while reliably detecting objects. The third category (privacy) considers problems where one wishes to run machine learning algorithms on sensitive data. It has been shown that seemingly innocuous analyses on such data can be exploited to reveal data individuals would prefer to keep private. Thus, nearly any algorithm that runs on patient or economic data falls under this set of problems. We devise solutions for each of these problem categories including (i) a fast tree-based model for explicitly trading off accuracy and model evaluation time, (ii) a compression method for the k-nearest neighbor classifier, and (iii) a private causal inference algorithm that protects sensitive data

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Bounded-Distortion Metric Learning

Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering. Although a greatly distorted metric space has a high degree of freedom to fit training data, it is prone to overfitting and numerical inaccuracy. This paper presents {\it bounded-distortion metric learning} (BDML), a new metric learning framework which amounts to finding an optimal Mahalanobis metric space with a bounded-distortion constraint. An efficient solver based on the multiplicative weights update method is proposed. Moreover, we generalize BDML to pseudo-metric learning and devise the semidefinite relaxation and a randomized algorithm to approximately solve it. We further provide theoretical analysis to show that distortion is a key ingredient for stability and generalization ability of our BDML algorithm. Extensive experiments on several benchmark datasets yield promising results

Compressive Embedding and Visualization using Graphs

Visualizing high-dimensional data has been a focus in data analysis communities for decades, which has led to the design of many algorithms, some of which are now considered references (such as t-SNE for example). In our era of overwhelming data volumes, the scalability of such methods have become more and more important. In this work, we present a method which allows to apply any visualization or embedding algorithm on very large datasets by considering only a fraction of the data as input and then extending the information to all data points using a graph encoding its global similarity. We show that in most cases, using only $\mathcal{O}(\log(N))$ samples is sufficient to diffuse the information to all $N$ data points. In addition, we propose quantitative methods to measure the quality of embeddings and demonstrate the validity of our technique on both synthetic and real-world datasets