Sparse Component Analysis (SCA) is an unsupervised dimensionality reduction method that can recover latent factors corresponding to distinct computations, from high dimensional neural activity. This ...

In the previous three articles, I explained the mechanism of PCA from scratch. Because you have the experience of manual calculations with NumPy, you understand what the library is doing behind the ...

In the previous article, we learned the basic concept of PCA. Based on the idea of "finding the direction where the data is most spread out," we tried every angle from 0 to 180 degrees in 1-degree ...

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

Part II: Unsupervised machine learning in R to cluster and identify candidate countries for international expansion, using PCA, K-Means, and DBSCAN.

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 ...

Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...

Various regulatory bodies have published ethical principles, codes, and/or guidelines for mental health practice globally. Although such guidelines may lend themselves equally relevant, there seems a ...

PCA, CPCA and PBA all identified three dietary patterns, with a common “traditional southern Chinese” pattern high in rice and animal-based foods and low in wheat products and dairy. Only this pattern ...