Author

M. Aggarwal

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Sep 2026

Scale-Invariant, Robust Sparse PCA for Large Data via Differentiable Penalties

Sparse PCA finds low-dimensional structure that loads on few features. Existing methods couple learning and feature selection by applying non-differentiable penalties that force retain-or-zero decisions during optimization. This eliminates features before the optimizer has established which ones matter, limits scalabil...

M. Aggarwal, V. Periwal · Transactions on machine lear... · 0 citations

Open access Sep 2026

Mapping Gene Expression to an Interpretable Semantic Space

Cell embeddings organize single-cell expression data, but their dimensions have no biological meaning, so clusters are interpreted afterward. We present MESIC (Mapping Expression to Semantic space with Interpretable Components), which builds the written knowledge about genes held in curated databases into the dimension...

Xiaoyu Duan, M. Aggarwal, V. Periwal · bioRxiv · 0 citations