aopeng Xi

Fast time series classification using numerosity reduction

Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many realtime applications. One way to mitigate this problem is to speed up the DTW calculations. Nonetheless, there is a limit to how much this can help. In this work, we propose an additional technique, numerosity reduction, to speed up one-nearest-neighbor DTW. While the idea of numerosity reduction for nearest-neighbor classifiers has a long history, we show here that we can leverage off an original observation about the relationship between dataset size and DTW constraints to produce an extremely compact dataset with little or no loss in accuracy. We test our ideas with a comprehensive set of experiments, and show that it can efficiently produce extremely fast accurate classifiers.

Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining

For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have from milliseconds to minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we can convert the ubiquitous nearest neighbor classifier into an anytime algorithm that can produce an instant classification, or if given the luxury of additional time, can utilize the extra time to increase classification accuracy. We demonstrate the utility of our approach with a comprehensive set of experiments on data from diverse domains.

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures

Shape matching and indexing is important topic in its own right, and is a fundamental subroutine in most shape data mining algorithms. Given the ubiquity of shape, shape matching is an important problem with applications in domains as diverse as biometrics, industry, medicine, zoology and anthropology. The distance/similarity measure for used for shape matching must be invariant to many distortions, including scale, offset, noise, articulation, partial occlusion, etc. Most of these distortions are relatively easy to handle, either in the representation of the data or in the similarity measure used. However, rotation invariance is noted in the literature as being an especially difficult challenge. Current approaches typically try to achieve rotation invariance in the representation of the data, at the expense of discrimination ability, or in the distance measure, at the expense of efficiency. In this work, we show that we can take the slow but accurate approaches and dramatically speed them up. On real world problems our technique can take current approaches and make them four orders of magnitude faster without false dismissals. Moreover, our technique can be used with any of the dozens of existing shape representations and with all the most popular distance measures including Euclidean distance, dynamic time warping and Longest Common Subsequence. We further show that our indexing technique can be used to index star light curves, an important type of astronomical data, without modification.

SAXually Explicit Images: Finding Unusual Shapes

Over the past three decades, there has been a great deal of research on shape analysis, focusing mostly on shape indexing, clustering, and classification. In this work, we introduce the new problem of finding shape discords, the most unusual shapes in a collection. We motivate the problem by considering the utility of shape discords in diverse domains including zoology, anthropology, and medicine. While the brute force search algorithm has quadratic time complexity, we avoid this by using locality-sensitive hashing to estimate similarity between shapes which enables us to reorder the search more efficiently. An extensive experimental evaluation demonstrates that our approach can speed up computation by three to four orders of magnitude.

Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems

The vast majority of visualization tools introduced so far are specialized pieces of software that run explicitly on a particular dataset at a particular time for a particular purpose. In this work we introduce a novel framework for allowing visualization to take place in the background of normal day-to-day operation of any GUI based operation system. Our system works by replacing the standard file icons with automatically created icons that reflect the contents of the files in a principled way. We call such icons Intelligent Icons. The utility of Intelligent Icons is further enhanced by arranging them in a way that reflects their similarity/differences. We demonstrate the utility of our approach on diverse applications.

Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces

Object recognition and content-based image retrieval systems rely heavily on the accurate and efficient identification of 2-D shapes. Features such as color, texture, positioning etc., are insufficient to convey the information that could be obtained through shape analysis. A fundamental requirement in this analysis is that shape similarities are computed invariantly to basic geometric transformations, e.g., scaling, shifting, and most importantly, rotations. And while scale and shift invariance are easily achievable through a suitable shape representation, rotation invariance is much harder to deal with. In this work, we explore the metric properties of the rotation-invariant distance measures and propose an algorithm for fast similarity search in the shape space. The algorithm can be utilized in a number of important data mining tasks such as shape clustering and classification, or for discovering of motifs and discords in large image collections. The technique is demonstrated to introduce a dramatic speed-up over the current approaches, and is guaranteed to introduce no false dismissals.

Efficiently finding unusual shapes in large image databases

Among the visual features of multimedia content, shape is of particular interest because humans can often recognize objects solely on the basis of shape. Over the past three decades, there has been a great deal of research on shape analysis, focusing mostly on shape indexing, clustering, and classification. In this work, we introduce the new problem of finding shape discords, the most unusual shapes in a collection. We motivate the problem by considering the utility of shape discords in diverse domains including zoology, microscopy, anthropology, and medicine. While the brute force search algorithm has quadratic time complexity, we avoid this untenable lethargy by using locality-sensitive hashing to estimate similarity between shapes which enables us to reorder the search more efficiently and thus extract the maximum benefit from an admissible pruning strategy we introduce. An extensive experimental evaluation demonstrates that our approach is empirically linear in time.

Converting non-parametric distance-based classification to anytime algorithms

For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have anywhere from several milliseconds to several minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we convert the ubiquitous nearest neighbor classifier into an anytime algorithm that can produce an instant classification, or if given the luxury of additional time, can continue computations to increase classification accuracy. We demonstrate the utility of our approach with a comprehensive set of experiments on data from diverse domains. We further show the utility of our work with two deployed applications, in classifying and counting fish, and in classifying insects.