Efficient Encoding and Embedding Strategies

EasyChair Preprint no. 13363

Abstract

Efficient encoding and embedding strategies are crucial in various fields, including natural language processing, computer vision, and speech recognition, as they enable effective data representation, storage, and processing. This paper provides a comprehensive overview of the key encoding and embedding techniques used in modern applications.

For text data, we discuss character encoding, word encoding (e.g., one-hot, TF-IDF, Word2vec, GloVe), and sentence/document encoding (e.g., bag-of-words, TF-IDF, sentence embeddings, transformer-based models). In the context of image data, we cover pixel-level encoding and feature-based encoding techniques, including handcrafted features and deep learning-based features. For audio data, we explore time-domain encoding (e.g., raw waveform, MFCC) and frequency-domain encoding (e.g., spectrogram, mel-spectrogram).

Furthermore, we delve into various embedding strategies, ranging from linear techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to non-linear approaches such as t-SNE and UMAP. We also discuss deep learning-based embeddings, including autoencoder-based and contrastive learning-based methods.

Efficiency considerations are a critical aspect of this work, as we examine computational efficiency (time and space complexity), memory efficiency (sparse vs. dense representations, quantization, and compression), and energy efficiency (hardware-aware optimization and low-power architectures).

Keyphrases: data representation, Embedding strategies, Image Embeddings, low-dimensional vectors, word embeddings

@Booklet{EasyChair:13363,
  author = {Ayuns Luz and Harold Jonathan},
  title = {Efficient Encoding and Embedding Strategies},
  howpublished = {EasyChair Preprint no. 13363},

  year = {EasyChair, 2024}}