In this article we provide a formulation of empirical bayes described by Atchade (2011) to tune the hyperparameters of priors used in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We see that it can be used to get a good initial choice for the parameters. It can also be used to guess an initial choice for hyper-parameters in grid search procedure even for the datasets where MCMC oscillates around the true value or takes long time to converge.

Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating, and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-conjugate hierarchical priors to explicitly model temporal changes of the model parameters. We also derive a novel variational inference scheme which overcomes the use of non-conjugate priors while maintaining the computational efficiency of variational methods over conjugate models. The approach is validated on three real data sets over three latent variable models.

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the state-of-the-arts in this literature.

A k-means clustering algorithmus implemented in p5.js It shows the clustering algorithm quite nicely in a visual way. I also liked p5.js a lot. The only thing which is neede is some kind of webserver to serve index.html, Click on the graphics to see how the points get clustered.

Driverless AI is the latest product from H2O.ai aimed at lowering the barrier to making data science work in a corporate context. The tool assists non-technical employees with preparing data, calibrating parameters and determining the optimal algorithms for tackling specific business problems with machine learning. At the research level, machine learning problems are complex and unpredictable -- combining GANs and reinforcement learning in a never before seen use case takes finesse. But the reality is that a lot of corporates today use machine learning for relatively predictable problems -- evaluating default rates with a support vector machine, for example. But even these relatively straightforward problems are tough for non-technical employees to wrap their heads around.

In this two-part series, we will explore text clustering and how to get insights from unstructured data. It will be quite powerful and industrial strength. The first part will focus on the motivation. The second part will be about implementation. This post is the first part of the two-part series on how to get insights from unstructured data using text clustering.

PCA (Principal Component Analysis) is probably the oldest trick in the book. PCA is well studied and there are numerous ways to get to the same solution, we will talk about two of them here, Eigen decomposition and Singular Value Decomposition (SVD) and then we will implement the SVD way in TensorFlow. From now on, X will be our data matrix, of shape (n, p) where n is the number of examples, and p are the dimensions. So given X, both methods will try to find, in their own way, a way to manipulate and decompose X in a manner that later on we could multiply the decomposed results to represent maximum information in less dimensions. I know I know, sounds horrible but I will spare you most of the math but keep the parts that contribute to the understanding of the method pros and cons.

This tutorial is intended for readers who are new to both machine learning and TensorFlow. If you already know what MNIST is, and what softmax (multinomial logistic) regression is, you might prefer this faster paced tutorial. Be sure to install TensorFlow before starting either tutorial. When one learns how to program, there's a tradition that the first thing you do is print "Hello World." Just like programming has Hello World, machine learning has MNIST. MNIST is a simple computer vision dataset.

Extracting meaningful knowledge from large and nonlinearly-connected data structures is of primary importance for efficiently utilizing data. Big data problems (e.g. 1 GB/s) often contain superpositions of multiple distinct processes, sources, or latent factors. Estimating or inferring the component distributions or statistical factors is called the mixture problem. Methods for solving mixture problems are known as mixture models [Everitt, 1996], and in machine learning they are used to define Bayes classifiers [Bishop, 2006]. Mixture models are a widely applicable pattern recognition and dimensionality reduction approach for extracting meaningful content from large and complex datasets. Only finite mixture models are described here, although countably or uncountably infinite numbers of mixture components are also possible [McAuliffe et al., 2006]. In terms of dimensionality reduction methods, Laplacian mixture models provide global and nonhierarchical analyses of massive datasets using scalable algorithms.