scispace - formally typeset
C

CJ Carey

Researcher at University of Massachusetts Amherst

Publications -  30
Citations -  20080

CJ Carey is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Python (programming language) & Nonlinear dimensionality reduction. The author has an hindex of 13, co-authored 28 publications receiving 9933 citations. Previous affiliations of CJ Carey include Google & Washington University in St. Louis.

Papers
More filters
Journal ArticleDOI

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Journal ArticleDOI

SciPy 1.0: fundamental algorithms for scientific computing in Python.

TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
Journal ArticleDOI

Machine learning tools formineral recognition and classification from Raman spectroscopy

TL;DR: This project applies machine learning techniques to improve mineral identification using Raman spectroscopy to find that optimal mineral spectrum matching performance can be achieved using careful preprocessing and a weighted-neighbors classifier based on a vector similarity metric.
Proceedings Article

Manifold warping: manifold alignment over time

TL;DR: This paper presents a novel framework for aligning two sequentially-ordered data sets, taking advantage of a shared low-dimensional manifold representation, and combines traditional manifold alignment and dynamic time warping algorithms using alternating projections.