Fast algorithms for nearest neighbor (NN) search have in large part focused on L2 distance. Here we develop an approach for L1 distance that begins with an explicit and exact embedding of the points into L2. We show how this embedding can efficiently be combined with random projection methods for L2 NN search, such as locality-sensitive hashing or random projection trees. We rigorously establish the correctness of the methodology and show by experimentation that it is competitive in practice with available alternatives.

Google has launched two new Live case lines for its Pixel phones that come with their own live wallpapers, and one of them's a lot more relaxing than the other. The Google Earth Live cases feature beaches, ice formations and other beautiful photos of our planet taken from the company's satellite imagery. While each case matches a specific Google Earth photo, their live wallpaper changes everyday using a rotation of curated images from the program. You'll also find a shortcut button on the home screen that you can tap to explore the specific location currently shown on your screen. The Google Trends Live case's wallpaper, on the other hand, might not always be as enjoyable to look at.

While the oil and gas industry has had its share of ups and downs over the past decade, many financial institutions are banking on a very slow growth of oil prices in 2017. Though some believe that the efficiency gains that the oil industry can capture are quickly coming to an end, this sentiment is only capturing hard technology specifically related to oil and gas. To help bring the O&G industry to the 21st century, technology from other industries needs to be incorporated, using many hard-earned years of expertise and different lines of thinking. Oilprice previously mentioned incorporating food industry technology to increase safety standards when fracking, but incorporating technology from the IT industry is something that the O&G industry as a whole can benefit from. Whether its neural networks, machine learning, fuzzy logic, case-based reasoning or expert systems, AI has the potential to transform the industry.

This artist's impression shows the planet Proxima b orbiting the red dwarf star Proxima Centauri, the closest star to the Solar System. The double star Alpha Centauri AB also appears in the image between the planet and Proxima itself. Proxima b is a little more massive than the Earth and orbits in the habitable zone around Proxima Centauri, where the temperature is suitable for liquid water to exist on its surface. It was just over 20 years ago--a blink of a cosmic eye--that astronomers found the first planets orbiting stars other than our Sun. All these new worlds were gas-shrouded giants like Jupiter or Saturn and utterly inhospitable to life as we know it--but for years each discovery was dutifully reported as front-page news, while scientists and the public alike dreamed of a day when we would find a habitable world. An Earth-like place with plentiful surface water, neither frozen nor vaporized but in the liquid state so essential to life. Back then the safe bet was to guess that the discovery of such a planet would only come after many decades, and that when a promising new world's misty shores materialized on the other side of our telescopes, it would prove too faraway and faint to study in any detail.

While the oil and gas industry has had its share of ups and downs over the past decade, many financial institutions are banking on a very slow growth of oil prices in 2017. Though some believe that the efficiency gains that the oil industry can capture are quickly coming to an end, this sentiment is only capturing hard technology specifically related to oil and gas. To help bring the O&G industry to the 21st century, technology from other industries needs to be incorporated, using many hard-earned years of expertise and different lines of thinking. Oilprice previously mentioned incorporating food industry technology to increase safety standards when fracking, but incorporating technology from the IT industry is something that the O&G industry as a whole can benefit from. Whether its neural networks, machine learning, fuzzy logic, case-based reasoning or expert systems, AI has the potential to transform the industry.

Unsupervised nearest neighbors is the foundation of many other learning methods, notably manifold learning and spectral clustering. Supervised neighbors-based learning comes in two flavors: classification for data with discrete labels, and regression for data with continuous labels. The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. The number of samples can be a user-defined constant (k-nearest neighbor learning), or vary based on the local density of points (radius-based neighbor learning). The distance can, in general, be any metric measure: standard Euclidean distance is the most common choice. Neighbors-based methods are known as non-generalizing machine learning methods, since they simply "remember" all of its training data (possibly transformed into a fast indexing structure such as a Ball Tree or KD Tree.). Despite its simplicity, nearest neighbors has been successful in a large number of classification and regression problems, including handwritten digits or satellite image scenes.

Is there any Approximate Nearest Neighbor (ANN) library in Python which supports custom distance function? None of them supports custom functions. Are there any libraries which has the same? Yes, I have used that, but it is slow for my need. That's why was looking for an ANN library.

$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.

Despite heavy investment in data management and monitoring platforms, the financial services industry still lacks real-time operational intelligence to enable better business decision-making and prevent systems and service failures and catastrophic trading errors. These outages expose institutions to undue risk and compliance violations that can cost organizations millions of dollars in financial losses and regulatory fines. They also undermine investor confidence and damage firm reputation. Modern financial markets have become more complex that ever fueled by the globalization of capital markets, including a variety of new securities, derivatives and indexes, the evolution of high-frequency trading platforms with millisecond execution windows, more stringent regulations and higher levels of interconnection among different players. This increased complexity is overwhelming legacy systems, resulting in overlooked information and missed opportunities to uncover hidden patterns, relationships and dependencies.

When it comes to knowledge technologies for customer engagement, Case-Based Reasoning (CBR) tops the list in guiding not only search but also decisions and process. Want us to help you do it in your organization?