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Hacarus’ Sparse Modeling technology requires small data sets for analysis.

This opens new opportunities to benefit from AI technology in areas where big data is not available or is too costly to collect.

In our blog we share knowledge and insights about our technology:

Sparse Modeling For IT Engineers

Using Sparse Modeling in AI: A Human Centric, Explainable Approach

This post introduces a technique called "Sparse Modeling" that can produce good analysis results, even if the amount of data is small. The article was written for engineers who want to start on machine learning and for those who have already experience with deep learning. Note: The original version of this article was published in Japanese on 機械学習プロジェクトにおける課題と、スパースモデリングに期待が高まる背景 Introduction The field of machine learning and particularly deep learning, based on data acquisition and collection of information through the cloud…

Release of spm-image : Python library for sparse modeling

We’ve released spm-image, Python library for sparse modeling and compressive sensing, in our GitHub. Its latest version includes k-SVD, which is one of popular dictionary learning algorithms, ADMM implementation of Lasso and ZCA whitening for preprocessing. We’ve created example notebooks as well so that you can get to know how to use this library.The basic concept of this library is as follows Implements sparse modeling and compressive sensing algorithms Depends on scikit-learn only Compliant with module and interface structure of scikit-learn as much…