These techniques help simplify complex data, making it easier to process and analyze, especially in the context of machine learning (ML). Depending on how they process the data, dimensionality reduction methods
ML requires large data sets to properly train and operate. There's a challenge typically associated with ML called thecurse of dimensionality. The idea behind this curse is that as the number of features in a data set grows, the ML model becomes more complex and begins to struggle to find ...
Traditional indexing methods struggle with the "curse of dimensionality," where the efficiency of search algorithms degrades as the number of dimensions increases. A good example is in document retrieval applications, such as a large repository of scientific articles, where each paper is represented a...
Here, only one element of the vector is "hot" (set to 1) to indicate the presence of that word. While simple, this approach suffers from the curse of dimensionality, lacks semantic information and doesn't capture relationships between words. Word embeddings, on the other hand, are dense ...
The challenge here is the “Curse of dimensionality”; you can be fast or accurate but can’t be both; you have to pick one. LanceDB first went for speed, then did a lot of tuning to improve accuracy. This leads to “Embeddings,” which are high-dimensional floating-point vector repres...
That can decrease search performance and has been called the “curse of dimensionality.” There are techniques to help mitigate this challenge, such as dimensionality reduction via vector quantization, which is a lossy data compression technique used in machine learning. It works by mapping vectors ...
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be pr...
Binary encoding is a memory-efficient encoding scheme as it uses fewer features thanone-hot encodingcategorical data. Further, It reduces the curse of dimensionality for data with high cardinality. Base N Encoding Before diving into BaseN encoding let’s first try to understand what is Base here...
Training data is often labeled to show the ML system what the “correct answer” is, such as a bounding box around a face in a face detector, or future stock performance in a stock predictor. Representation—It refers to the encoded representations of objects in the training data, such as...
NNs have proven to represent the underlying nonlinear input-output relationship in complex systems. Unfortunately, dealing with such high dimensional-complex systems are not exempt from the curse of dimensionality, which Bellman first described in the context of optimal control problems [15]. However,...