In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Unlike PCA (maximum variance) or ICA (maximum independence), ForeCA finds components that are maximally forecastable. This makes it ideal for time series analysis where prediction is often the primary ...

If you’d like an LLM to act more like a partner than a tool, Databot is an experimental alternative to querychat that also works in both R and Python. Databot is designed to analyze data you’ve ...

Abstract: In this paper, we propose a novel algorithm to solve the row-sparse principal component analysis problem without relying on any data structure assumption. Sparse principal component analysis ...

Python for Data Analysis/ ├── Month_1: Python Foundations and Data Manipulation │ ├── Week_1: Introduction and Environment Setup │ │ ├── Lecture/ │ │ ├── Practice/ │ │ ├── Assignments/ │ │ └── Data/ │ ...

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The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a compelling case with benchmark data that standard techniques perform poorly. The ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mass spectrometry imaging (MSI) is constantly improving in spatial resolving power, ...

Abstract: Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic ...