Many important applications, including signal reconstruction, parameter estimation, and signal processing in a compressed domain, rely on a low-dimensional representation of the dataset that preserves {\em all} pairwise distances between the data points and leverages the inherent geometric structure that is typically present. Recently Hedge, Sankaranarayanan, Yin and Baraniuk \cite{hedge2015} proposed the first data-aware near-isometric linear embedding which achieves the best of both worlds. However, their method NuMax does not scale to large-scale datasets. Our main contribution is a simple, data-aware, near-isometric linear dimensionality reduction method which significantly outperforms a state-of-the-art method \cite{hedge2015} with respect to scalability while achieving high quality near-isometries. Furthermore, our method comes with strong worst-case theoretical guarantees that allow us to guarantee the quality of the obtained near-isometry. We verify experimentally the efficiency of our method on numerous real-world datasets, where we find that our method ($<$10 secs) is more than 3\,000$\times$ faster than the state-of-the-art method \cite{hedge2015} ($>$9 hours) on medium scale datasets with 60\,000 data points in 784 dimensions. Finally, we use our method as a preprocessing step to increase the computational efficiency of a classification application and for speeding up approximate nearest neighbor queries.

Symmetric Nonnegative Matrix Factorization (SNMF) models arise naturally as simple reformulations of many standard clustering algorithms including the popular spectral clustering method. Recent work has demonstrated that an elementary instance of SNMF provides superior clustering quality compared to many classic clustering algorithms on a variety of synthetic and real world data sets. In this work, we present novel reformulations of this instance of SNMF based on the notion of variable splitting and produce two fast and effective algorithms for its optimization using i) the provably convergent Accelerated Proximal Gradient (APG) procedure and ii) a heuristic version of the Alternating Direction Method of Multipliers (ADMM) framework. Our two algorithms present an interesting tradeoff between computational speed and mathematical convergence guarantee: while the former method is provably convergent it is considerably slower than the latter approach, for which we also provide significant but less stringent mathematical proof regarding its convergence. Through extensive experiments we show not only that the efficacy of these approaches is equal to that of the state of the art SNMF algorithm, but also that the latter of our algorithms is extremely fast being one to two orders of magnitude faster in terms of total computation time than the state of the art approach, outperforming even spectral clustering in terms of computation time on large data sets.

Some of the popular anomaly detection techniques are Density-based techniques (k-nearest neighbor,local outlier factor,Subspace and correlation-based, outlier detection, One class support vector machines, Replicator neural networks, Cluster analysis-based outlier detection, Deviations from association rules and frequent itemsets, Fuzzy logic based outlier detection and Ensemble techniques. RapidMiner provides an integrated environment for machine learning, data mining, text mining, predictive analytics and business analytics. Scikit-learn is an open source machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. You may also live to read, Top Business Intelligence companies, Open Source and Free Business Intelligence Solutions, Cloud – SaaS – OnDemand Business Intelligence Solutions, Top Free Extract, Transform, and Load, ETL Software, Freemium Cloud Business Intelligence Solutions, Top Embedded Analytics Business Intelligence Software, Top Dashboard Software, and Top Data Visualization Software.

MS/PhD in Computer Science, Statistics, or Business 3 years of experience in Machine Learning or related field Proven track record of working on complex modular projects, and assuming a leading role in such projects Highly motivated, self-driven, capable of defining own design and test scenarios Expert knowledge of SQL. Able to optimize complex queries Able to execute research projects, and generate practical results and recommendations Experience with K-means clustering, factor analysis, regression, neural networks, decision trees, SVM, Naive Bayesian classifications, and different simulation approaches like Monte Carlo methods a plus Experience with scripting languages (i.e.Javascript, Python) preferred Experience with natural language processing, lexical analysis, and semantic processing a plus PERKS!

For a sequence model to be both expressive and parsimonious in its parameterization, it is crucial to equip the model with memory thus allowing its prediction at time t to depend on previously seen inputs. Recurrent neural networks form an expressive class of nonlinear sequence models. Through their many variants, such as long-short-term-memory [HS97], recurrent neural networks have seen remarkable empirical success in a broad range of domains. At the core, neural networks are typically trained using some form of (stochastic) gradient descent. Even though the training objective is non-convex, it is widely observed in practice that gradient descent quickly approaches a good set of model parameters.

This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017). Beyond exposing the potential pitfalls of ABC based posterior probabilities, the review emphasizes mostly the solution proposed by Pudlo et al. (2016) on the use of random forests for aggregating summary statistics and and for estimating the posterior probability of the most likely model via a secondary random fores.

Can you put order to space and time? This was the challenge posed to competitors of the Draper Satellite Image Chronology Competition (Chronos) which ran on Kaggle from April to June 2016. Over four-hundred Kagglers chose a path somewhere between man and machine to accurately determine the chronological order of satellite images taken over five day spans. In collaboration with Kaggle, Draper designed the competition to stimulate the development of novel approaches to analyzing satellite imagery and other image-based datasets. In this spirit, competitors' solutions were permitted to rely on hand annotations as long as their methodologies were replicable.

O'Reilly has released the results of the 2016 Data Science Salary Survey. This survey is based on data from over 900 respondents to a 64-question survey about data-related tasks, tools, and the salary they receive from doing/using them. The median salary reported in the survey was US 87,000; amongst data scientists in the US, the median salary was US 106,000. Appropriately for a survey about data science, O'Reilly doesn't merely report aggregate statistics from the survey; they fit a linear regression model for a data, and extact coefficients from the model indicative of salary "bumps" (or downgrades) attributable to demographic factors. Factors that tended to increase salary included: working in cloud computing environments; working with Python; and being older.

This is the first article from a series of articles I will be writing about the math behind SVM. There is a lot to talk about and a lot of mathematical backgrounds is often necessary. However, I will try to keep a slow pace and to give in-depth explanations, so that everything is crystal clear, even for beginners. Part 1: What is the goal of the Support Vector Machine (SVM)? Part 2: How to compute the margin?

Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions. In reality, these values would not be randomly generated -- they would instead be the output of your scoring function f. Let's exponentiate the output of the scoring function, yielding our unnormalized probabilities: Figure 2: Exponentiating the output values from the scoring function gives us our unnormalized probabilities. Figure 4: Taking the negative log of the probability for the correct ground-truth class yields the final loss for the data point. To examine some actual probabilities, let's loop over a few randomly sampled training examples and examine the output probabilities returned by the classifier: Note: I'm randomly sampling from the training data rather than the testing data to demonstrate that there should be a noticeably large gap in between the probabilities for each class label.