**Principle Components Analysis (PCA)** is an unsupervised method primary used for dimensionality reduction within machine learning.  PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.

Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)

**Co-Correcting** is a noise-tolerant deep learning framework for medical image classification based on mutual learning and annotation correction. It consists of three modules: the dual-network architecture, the curriculum learning module, and the label correction module.

Co-Correcting

Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction

**ESACL**, or **Enhanced Seq2Seq Autoencoder via Contrastive Learning**, is a denoising sequence-to-sequence (seq2seq) autoencoder via contrastive learning for abstractive text summarization. The model adopts a standard [Transformer](https://paperswithcode.com/method/transformer)-based architecture with a multilayer bi-directional encoder and an autoregressive decoder. To enhance its denoising ability, self-supervised contrastive learning is incorporated along with various sentence-level document augmentation.

Year	2000
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