1. A method, comprising steps of: in a computer system a) receiving data comprising a plurality of measurements m of multidimensional data points (MDDPs), each data point having n features wherein n>3, wherein m>>n and wherein the data forms a matrix A=m×n; b) applying randomized lower and upper triangular matrix (LU) decomposition to matrix A to obtain a dictionary D in the form of a matrix k×n where k is the rank of matrix A and k<<m; c) embedding dictionary D in a lower dimension space that has a dimension lower than n, thereby obtaining an embedded dictionary D; d) in the lower dimension space and based on embedded dictionary D, classifying a MDDP or a newly arrived MDDP (NAMDDP) as an anomaly; e) preparing an alert related to the anomaly; and f) sending the alert to a data client system, whereby the combined reduction in the size of both m and n reduces computer processing requirements, thereby enhancing anomaly detection performance.