We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.

/pdf/visualizing-data-using-t-sne-2w1n304iq0.pdf

Visualizing Data using t-SNE

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

https://axon.cs.byu.edu/~martinez/classes/678/Presentations/Yao.pdf

Extreme learning machine: Theory and applications

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

With mobility, security, intelligence and other advantages, autonomous underwater vehicle (AUV) becomes an indispensable instrument in the complex underwater environment Owing to the independence of external signal (such as GPS) which is restricted or invalid in the water, inertial navigation system (INS) has become the most suitable navigation and positioning system for Underwater Vehicles However, as the excessive reliance of sensor data, the precision of INS can be affected by sensor data especially heading angle data from low-cost sensor such as attitude and heading reference system (AHRS) and digital compass Therefore, how to fuse low-cost sensor information to get more accurate data becomes the key to improve navigation accuracy Based on the original Extreme Learning Machine (ELM) algorithm, the Optimally Pruned Extreme Learning Machine (OPELM) algorithm is presented as a more robust and general methodology in 2010, which make it possible to realize data fusion by using a more reliable network In this paper, we proposed a method of data fusion which using Optimally-Pruned Extreme Learning Machine (OPELM) to improve the accuracy of heading angle from AHRS and digital compass Our method has already been demonstrated by a range of real datasets, and it outperforms current available Kalman Filtering algorithms in efficiency

Data Fusion Using OPELM for Low-Cost Sensors in AUV

A general-purpose useful parameter in data analysis is the intrinsic dimension of a data set, corresponding to the minimum number of variables necessary to describe the data without significant loss of information The knowledge of this dimension also facilitates most non-linear projection methods We will show that the intrinsic dimension of a data set can be efficiently estimated using Curvilinear Component Analysis; we will also show that the method can be applied to the Blind Source Separation problem to estimate the number of sources in a mixing

Extraction of intrinsic dimension using CCA-Application to blind sources separation.

The fast sonar-based object recognition turns out to be one of the most challenging topics in the underwater signal analysis. In this paper, we try to develop a fast benthic object recognition model via the extreme learning machine (ELM) on the basis of the structured geometrical feature extraction. Geometrical features such as major and minor axis, eccentricity, circularity and so on are employed to construct learning samples of ELM. The classifier based on ELM is used to recognize the target objects in sonar images. It has been shown in the simulation experiments that the proposed model could keep a quite good recognition performance with a much fast speed.

http://ieeexplore.ieee.org/iel7/7395976/7401802/07401948.pdf

A fast sonar-based benthic object recognition model via extreme learning machine

This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising need of processing data generated by natural person (a human being), also known as personal data. Spiking Neural networks are the core method behind it: suitable for a low latency energy-constrained hardware, enabling local training or re-training, while not taking advantage of scalability available in the Cloud.

Spiking networks for improved cognitive abilities of edge computing devices

In Functional Data Analysis (FDA) multivariate data are considered as sampled functions. We propose a non-supervised method for finding a good function basis that is built on the data set. The basis consists of a set of Gaussian kernels that are optimized for an accurate fitting. The proposed methodology is experimented with two spectrometric data sets. The obtained weights are further scaled using a Delta Test (DT) to improve the prediction performance. Least Squares Support Vector Machine (LS-SVM) model is used for estimation.

/pdf/gaussian-fitting-based-fda-for-chemometrics-wux22a0n2l.pdf

Amaury Lendasse

Papers

Data Fusion Using OPELM for Low-Cost Sensors in AUV

Extraction of intrinsic dimension using CCA-Application to blind sources separation.

A fast sonar-based benthic object recognition model via extreme learning machine

Spiking networks for improved cognitive abilities of edge computing devices

Gaussian fitting based FDA for chemometrics