Post by sabbirislam258 on Feb 14, 2024 7:20:30 GMT
If a value is greater than 0, it is mapped to 1, otherwise it is marked as 0. Therefore, it transforms high-dimensional data into a significantly lower-dimensional permutation. Faster matching search. Formula The formula is: Binary quantization formula. Photo by the author. Here is an example of how binary quantization works on a vector. Example of BQ Graphical representation of binary quantization. Photo by the author. power Fastest search, outperforming both scalar and product quantization techniques. Reduces the memory footprint by a factor of 32 . Limitations High rate of information loss. Vector components require a mean equal to approximately zero. Poor performance on low-dimensional data due to high information loss.
Re-scoring is essential for best results. Vector Brazil Telemarketing Data databases such as Qdrant and Binay offer binary quantization. 2. Scalar quantization Scalar quantization converts floating point or decimal numbers to integers. It starts with identifying the minimum and maximum value for each dimension. The identified range is then divided into several bins. Finally, each value in each dimension is assigned to a bin. The level of precision or detail in a quantized vector depends on the number of bins. More bins result in greater accuracy by capturing finer details. Therefore, the accuracy of vector search also depends on the number of bins. Formula The formula is: Scalar quantization formula. Photo by the author. Here is an example of how scalar quantization works on a vector. Example of SQ Graphical representation of scalar quantization. Photo by the author. power Important memory optimization Small information loss. A partially reversible process.
Fast compression. Scalable search due to small information loss. Limitations Slight reduction in search quality. Low-dimensional vectors are more susceptible to information loss because each data point contains significant information. Vector databases such as Qdrant and Milwas offer scalar quantization. 3. Product quantization Product quantization divides the vector into subvectors. For each section, calculations are made using center points, or centroids. Clustering Algorithms . Their nearest centroids then represent each subvector. Similarity search in product quantization works by dividing the search vector into the same number of subvectors. Then, a list of similar results is constructed in ascending order of distance from the centroid of each subvector to each query subvector. Because the vector search process compares the distance of the query subvector to the centroids of the quantized vector, the search results are less accurate.
Re-scoring is essential for best results. Vector Brazil Telemarketing Data databases such as Qdrant and Binay offer binary quantization. 2. Scalar quantization Scalar quantization converts floating point or decimal numbers to integers. It starts with identifying the minimum and maximum value for each dimension. The identified range is then divided into several bins. Finally, each value in each dimension is assigned to a bin. The level of precision or detail in a quantized vector depends on the number of bins. More bins result in greater accuracy by capturing finer details. Therefore, the accuracy of vector search also depends on the number of bins. Formula The formula is: Scalar quantization formula. Photo by the author. Here is an example of how scalar quantization works on a vector. Example of SQ Graphical representation of scalar quantization. Photo by the author. power Important memory optimization Small information loss. A partially reversible process.
Fast compression. Scalable search due to small information loss. Limitations Slight reduction in search quality. Low-dimensional vectors are more susceptible to information loss because each data point contains significant information. Vector databases such as Qdrant and Milwas offer scalar quantization. 3. Product quantization Product quantization divides the vector into subvectors. For each section, calculations are made using center points, or centroids. Clustering Algorithms . Their nearest centroids then represent each subvector. Similarity search in product quantization works by dividing the search vector into the same number of subvectors. Then, a list of similar results is constructed in ascending order of distance from the centroid of each subvector to each query subvector. Because the vector search process compares the distance of the query subvector to the centroids of the quantized vector, the search results are less accurate.